{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "08_Multilayer_Perceptron",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "bOChJSNXtC9g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Multilayer Perceptron (MLP)"
      ]
    },
    {
      "metadata": {
        "id": "OLIxEDq6VhvZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png\" width=150>\n",
        "\n",
        "In this lesson, we will explore multilayer perceptrons which are a basic type of neural network. We will implement them using PyTorch.\n",
        "\n",
        "**Note**: This notebook is an introduction to MLPs in PyTorch so we won't follow proper machine learning techniques to keep things short and simple (class balance in train/test splits, validation sets, early stopping, etc.).  We will implement best practices in the next notebook.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "VoMq0eFRvugb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Overview"
      ]
    },
    {
      "metadata": {
        "id": "qWro5T5qTJJL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/mlp.png\" width=450>\n",
        "\n",
        "$z_2 = XW_1$\n",
        "\n",
        "$a_2 = f(z_2)$\n",
        "\n",
        "$z_3 = a_2W_2$\n",
        "\n",
        "$\\hat{y} = softmax(z_3)$ # classification\n",
        "\n",
        "*where*:\n",
        "* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n",
        "* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n",
        "* $z_2$ = outputs from first layer's weights  $\\in \\mathbb{R}^{NXH}$\n",
        "* $f$ = non-linear activation function\n",
        "* $a_2$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n",
        "* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n",
        "* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)\n",
        "\n",
        "This is a simple two-layer MLP. \n",
        "\n",
        "* **Objective:**  Predict the probability of class $y$ given the inputs $X$. Non-linearity is introduced to model the complex, non-linear data.\n",
        "* **Advantages:**\n",
        "  * Can model non-linear patterns in the data really well.\n",
        "* **Disadvantages:**\n",
        "  * Overfits easily.\n",
        "  * Computationally intensive as network increases in size.\n",
        "  * Not easily interpretable.\n",
        "* **Miscellaneous:** Future neural network architectures that we'll see use the MLP as a modular unit for feed forward operations (affine transformation (XW) followed by a non-linear operation)."
      ]
    },
    {
      "metadata": {
        "id": "Jq65LZJbSpzd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training"
      ]
    },
    {
      "metadata": {
        "id": "kfi_YArvjzrg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "*Steps*:\n",
        "\n",
        "1. Randomly initialize the model's weights $W$ (we'll cover more effective initalization strategies in future lessons).\n",
        "2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities.\n",
        "3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n",
        "  * $z_2 = XW_1$\n",
        "  * $a_2 = max(0, z_2)$ # ReLU activation\n",
        "  * $z_3 = a_2W_2$\n",
        "  * $\\hat{y} = softmax(z_3)$\n",
        "  * $J(\\theta) = - \\sum_i y_i ln (\\hat{y_i}) $\n",
        "4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n",
        "   * $ \\frac{\\partial{J}}{\\partial{W_{2j}}} = a_2\\hat{y}, \\frac{\\partial{J}}{\\partial{W_{2y}}} = a_2(\\hat{y}-1) $\n",
        "   * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_2}}  \\frac{\\partial{a_2}}{\\partial{z_2}}  \\frac{\\partial{z_2}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $\n",
        "   \n",
        "5. Apply backpropagation to update the weights $W$ using gradient descent. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n",
        "  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$\n",
        "6. Repeat steps 2 - 4 until model performs well."
      ]
    },
    {
      "metadata": {
        "id": "XtKqNioAayCy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data"
      ]
    },
    {
      "metadata": {
        "id": "X3OrtMpFayFC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We're going to first generate some non-linear data for a classification task."
      ]
    },
    {
      "metadata": {
        "id": "pvkfS3JZOMgB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load PyTorch library\n",
        "!pip3 install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9NfIz_4OPYpG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from argparse import Namespace\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cdcWUP-tTHj0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Arguments\n",
        "args = Namespace(\n",
        "    seed=1234,\n",
        "    num_samples_per_class=500,\n",
        "    dimensions=2,\n",
        "    num_classes=3,\n",
        "    train_size=0.75,\n",
        "    test_size=0.25,\n",
        "    num_hidden_units=100,\n",
        "    learning_rate=1e-0,\n",
        "    regularization=1e-3,\n",
        "    num_epochs=200,\n",
        ")\n",
        "\n",
        "# Set seed for reproducability\n",
        "np.random.seed(args.seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K2O38IuNayR5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Generate non-linear data\n",
        "def generate_data(num_samples_per_class, dimensions, num_classes):\n",
        "    # Make synthetic spiral data\n",
        "    X_original = np.zeros((num_samples_per_class*num_classes, dimensions))\n",
        "    y = np.zeros(num_samples_per_class*num_classes, dtype='uint8')\n",
        "    for j in range(num_classes):\n",
        "        ix = range(num_samples_per_class*j,num_samples_per_class*(j+1))\n",
        "        r = np.linspace(0.0,1,num_samples_per_class) # radius\n",
        "        t = np.linspace(j*4,(j+1)*4,num_samples_per_class) + \\\n",
        "        np.random.randn(num_samples_per_class)*0.2 # theta\n",
        "        X_original[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
        "        y[ix] = j\n",
        "\n",
        "    # Stack\n",
        "    X = np.hstack([X_original])\n",
        "\n",
        "    return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q14QCqKxUS2v",
        "colab_type": "code",
        "outputId": "78bc9bff-0db3-41ee-8297-7377c20809a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Generate X & y\n",
        "X, y = generate_data(num_samples_per_class=args.num_samples_per_class, \n",
        "                     dimensions=args.dimensions, num_classes=args.num_classes)\n",
        "print (\"X: {0}\".format(np.shape(X)))\n",
        "print (\"y: {0}\".format(np.shape(y)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: (1500, 2)\n",
            "y: (1500,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jgVjStv8VnX2",
        "colab_type": "code",
        "outputId": "5eb7823c-34fd-4587-b9f0-c24f7e06d2dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize data\n",
        "plt.title(\"Generated non-linear data\")\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, s=25, cmap=plt.cm.Spectral)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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F0LTNVFZm8n0Now9VFSjK6NEPw53PdD097DpF6VPdcwKPJ3HEhbauDxAMNuct\nAa4FwlOSd13X06iqieMU/208nnjJZ+f19pFMpibJpC852kihPUlcecNyeroSNO3pRVUU5syrYuYc\n19SXzVg4QuDzuXtrV1y7HEWBfbt6aW8dyHuLD/KbX+4kkTDzJvJ9e3r5k//lz99PcnKj6wPAfkIh\nVxBks71Eo4sAlUhkN4aRyLVMoGlZotHFlNsr1vXiinuKAuHwAaLRpYCdC5nShly38Pvb0PVkUSjV\nYF/HKYRZDdeKFcUmGGzN9xHCjZk2zSCW5SMY7MjfZxBNc8b8LIQgp80WcPeMi9vYthdNSw5po5HJ\nFH5PipJFVbPYdhBQ0LQ4qmpjmhEOJQmM19ufF9iF+SSwbV/RYsKyfDiOUdK/XBy5qroLpVRKCu0T\nESm0JwnHFvR2J7FM9we7a0cXP398GwBb/9CKZTksPKWODZ9ZjcfQuOqGlQA8+7P3+M0vdhbdKxEv\nNp8n41k2bzxYIrSFELz24l6a9vQSrvBx4aWLCUdkSMuJgK7H8Pl6EUIhlaor0rICgU6goLkZRgyf\nrwvTDODxJIru4/HEUNVsiUYHIESpMHI19f14vf2AwDTDxGLzAYVweA9eb2xI/2Ihq6qQSlWSStVT\nVbWzZN96uJBXFAvDiOHxxErajvQMcBcHjmPgOAaZTGVJ7HgyOQNNy+DxxBFCJZOpIpGYQTh8AF2P\noSgCIRQCgXbi8TkEAq34fF2oqo1l+RBCz32OAtMMEostKPn8FMXK7VFn8nvUoOI45bzUDRKJ6pyJ\n3N3TTiRmUm4xkExOw+frKRL8jgPZrPRrOVGRQvsokUxkSadMamtLqxI5juDJn2ylt7tYc9n2dht9\nQ9KKbnmrhaqaAFdetzx/bv0VS3jjd00k4gXz3aC2MhSvv/RP++yT7/HbZ3flj/fv6eXzf3kemiY9\nU482uh4vegknkzOxrCNTsUrXB4hE9uTjpL3efvr7F+M4PqDUHAygqiZeb19Z4TdUWx5KOl2FrrcN\nE6ZKkWe3prnFNpLJaSUpQMs9y22jlAjbcnverjY+sjZt2yqmGcIwYvkiI7atkkpNI52egabFCQQ6\n8Hp3YppBkskZuILTSzS6BE2LI4QHVTUJBDqwbQ2PR6CqAlW10PVuhLDw+QbyQnJouJg7nwSBQFvR\nnjlQtIDxeqNoWpZYrDFnQVBRFAdFIeekpxMItObDz1yTfPlVihBe+vuXEInsRtNMHEcjna7PWQAk\nJyJSaB9hhBA889g2/vBGM+mUyeKl9Vx306lUVgfybR7/8Tu881ZpqlDbLn1TtbcMsG9PD+Gwl9r6\nELpHY+2F8/nNL3fm2y88pY796IBCAAAgAElEQVSO1hgDUfcFMnNOBWs/UloXe/uW9qLj/Xv72La5\nlVPPmHVYc5aMhSAUas5rtZpmoqoH6O9fypHIp+33dxUlNtG0LH5/F4nEbABMM1yUotO2NdLpGny+\nnpJ72baBEIOvBQe/vxNVzWCaIdLpBvz+TnTdfZbjKNi2kXeYGsQ1+WYZD5pm4/f3lRHSCo5DWSe0\noQyazbPZCOl0LbbtIxLZiWGkURTQddeJLJ2uJRJpypucDSOGotg5j3CbcLgpp2k7qKoz4nMNI1mk\n1ZZj+Nw1LVWygPF4BvB4+ggGW4ue5TgCaMHjAY8nhWHE6elZxWivatsO0te3Ck1L4ThD/36SExH5\n1z3CvLe1nVd+swcn97t+/90Onv3Z+3ziNtctPNqfYusfSgW2z6+z6JRa/vDmwaLzB/f38dD/eRmv\nV+PUM2Zy/adP56NXL2X2/Cr27OymYXqENWfPprc7wVuvHcDn0znnI/Pz++FjoShSyz7aqGoWXS82\nQ7te0Zm8Njy++2TweGJksxGEKN3fLKYgCJLJGQSDHkyzJ6eJ1eI4ftLpOrzeXnTdFTKO45pbB/tH\nInvyWc2EcM3pgwIbXOescuZd14EsmdMaM7n+pZr0SCZuFw1VLbUQFPefycCAl2y2Ck1LEQodQNcT\nJY5oup4lGGwvcTjzeFxBGgy25Mpxjo1leVFVu8ixbvhcLMuPolj4fG5seDYbRghXmx4yA7ze3pLF\ngcdTLPBV1SEYPEgiMW+MkSnYdmCMNpITASm0jzDN+/ryAnuQ7s7CCzuTtjCzxY5kmq7w6T89i8bF\ntdhC8N6WdizLIRj0EBtwf8SZjM3GVw+waFkdp581m6Urp7F05bT8PWrrQ1x2zegZolacPoPOtg/y\nWs28hdWsOF16mR9tHMeDbXuLNFJ3j3X8Wbf8/jb8/nY0zUYIcnun83EcH5lMNYYRzWuAtq2TTtcM\n6a0AjfT3F0cnuGbhRQSDrXg8A6iqTTDYhqpaOe28kIbU1VqLY6sHHc9M03WaGi6ETTNMJlOdc9jy\noGlZdD2DbRsYRrFpftDJzE0m4kfTkmhasdAeFI5CuPHIPt8islnXghAKNZct+OF+Hh5MM1BmoeAu\nWIfPaziD/SzLSyo1E9NM5CwQVk6zNdF1K9dWAwSVlTvyiwTL8mKaobznvRCu09x4FwrltjckJy9S\naB9h5jZWo2oKzhBTd119YX+priHE/EU1RUU6TlnewOJl7gv1ps+dSXdnnGzW5sn/2kJsoLfo/m0t\nMU6f4Ngu/dgpVNX4adrdQzji4yOXLEJVj159YskgKqlUPYGAm4bTtjWSyXqg/N7xcFwv7OLCFoaR\nIBzeQzS6jGy2iljMTcghhJsIpHRPM0EotB9FschmK8hkalEUE8Pow+OJ5e+tae5+bjxe7tWgMFSD\nB9D1RFmBLYSC46hoWgohVEwzQipV8NoOhfbi8/XmhXA6XUsyOavo+tD9YiEgFpsJaHg8sZwn+yb8\n/gip1HQ0rViLHsS2PSST08hma8hme/OC002YUpdvMxK2rZNIzMzlJK8GNCwrRDpdh8fTTyTSNMz7\n2yYQ6Cz6PNzFmk0yWYthRNF1M9e2sHfvLgp8OA4YRmEurvVD5l6QFJBC+whzyooGzl/XyB/eOEg6\nabJ4WR2XXVvQgBVF4VOfO5Pnn3qfnq4E9dNCXHrN0qJ71Na7DkoNM8Ls210Q2pqu0Li4homiKApn\nnzePs8+bN+F7SCZGOt1AJlOFYQxgmpGy4TsjoaoZVLU0wY7Hk8LjGcA0K8hmq0bImOV6eMMu/H7X\n8dHr7culHI2XaLPu82wUxcE0g0PCwSCbDaBpTj70a9AMPhzXIayCQKArb6o2jBj9/YtymchE7hmF\nPrqexOPpxzQrAAXTDOWFOgzun0dyXvIFLT0YjGPb3lyIllk0hlhsLpYVyaclHRhYiM/XjapmyWYr\n8jHXyeSM3EKg+DO2LINYbMEIDoMqXu9A2f3tcmZ/XbdQlP6cdcUsaptMVmNZETKZKhTFobZ2H7Yd\nB1QSielDsrdJJFJoH3EUReHK61ew7oolZNIWCxfX09VV7IQSjni5/mY313cqleXlX+8lPpBhyfJ6\nlp9WMFdfcd1y4rEM+3b34vN5WHPObJYsl6UCpypCGGQytYfcz7YDWJY/n6t7KOMxsbsZwwrCdTBp\nSbmMYe493axgQoBleXJpQAWGkSCdrkVRrPw+eDmy2RpUNVu0t6xpJj5fD8lkAF2Pl5jevd4BDGOA\ndLqGeHx+zlGMIf0FkchuLMtXEgpmGDESiRkoipt21LY9pFL1mObwBa5KOl3YIlBV19Erk6mkv38Z\nFRUf5LV7y/IRjS4YVWCWK5JSuFYqvF0rS2lMuBAKmpakqqotd7aB3t5FgIJh9BKJ7AKcnLOduyXm\n93egaWls20cq1cCRcGiUTA2k0D5K+PwefP7SF+qObR1sfbsVr6HxoQvm8fiP3qEpp01vfHU/V1y7\njPPWLQTAHzD4zOfPJpO20HQVXZdOYyc+Ap+vE11P4DhGzjFMJx6fnatXnR1S7zmQy6ZlY1l+EolZ\nI1SnKhdfPfIIXGE+uEdbaKuqAp+vp2xCj0Esy61BHQyWOlsOjkNRrLLPVxTw+XpJperKxoQP1aSH\n4jgeLCtCf/8yNC2J4/jG9KAOBA7m9qUdAoFWEomZ9Pcvw+vtQVEcMpnaEUPfBnGLpESLfBUGk7dk\nMtWEQk0lCyPL8uXCx7L5OQ+mby18JgfwehXAIRxuHpLxzQ2Pc1O1FixwXm83bnERO+dN7yOVqpvQ\nAnF0BguYeJGLhMlDCu0jRCKe4c2X96NpKh86f26J93a0L8UTP9nC+1vb845q77x1MO9oBmCZDps3\ntuSF9iBeX/GfSQjB1k0ttLXEaFxSy6Klw6spSY4nRkt8MpxgsBm/v7AnqusJBgYWY1kR+vpWYRi9\neDyuVurxJPPt3P/bxGILS+6ZTtcRCrUwdD96dM/tAqWFOpyc49mQ763lI5msz+371gBuSJnHM5Df\nK3eduFwt1zQrc4ldSk3riiLQ9QyWFUCI3jLjtDDNgtUhmw3lNE0AFdseO/ZdVdP4/V1507ammfj9\nnWQyNSWJV0bDcXz095+SL5Iy6HDomvjdIimqWrzXbttBHCcLFOdZGI7HE8MwomWsCv0lFpfhseKa\nlkDTkphm8IilMvV6u3I+GdlcspfZ+XlKji1SaB8B2g4O8MN/fTPvJb7p9QP88RfPoaLS/cEMRNM8\n/Hcv09NV/JIaKrAHsWyHaF+Kja/sx/DpnH3+PLze4j/T4z96h42v7kcIeOmFXVx8+WLWX3nKUZqd\n5HBwnZX2DSmWESUaXTRCqJcoSXhiGDFCoX3E426WsWw2QiBwsKx52uOJ4yYfKah3ruexAIJAwbva\nNX370bQ0iiLGLcRt293nNc3OXDlL19Fr+HxMs5KBgUV4vT0IoeZKXg6ahhUGBhYQCLRhGNGifXXL\n8pHJVFJZ+UHZMTmOn/7+JXi9fUQiQaJRH6NrfYMLlUIbTUuV5EJ3Y6vFGPcqc/eSIimCYPAAXm8U\nIewiS4Vp+kmna0vC/8qZ0l0P83L52kudAcuhqoJIZB/9/eOrOe5uFcTIZivL+FvYuVrm7nfO40kT\nDLbQ339oKVslRwYptI8AL/92T1FYV9vBAV75zR6uuM6tD/zqi/tKBPYggbBBMlZ4AU+fEeE7/+f3\n9Ocyo21+o5nbv7SWQND9IfV0JXhn08G816llOmx6rZkLL10szefHFTaBQDteb0+RgND1DD5fF8nk\n7JIebsGLYhPwoMk4m42QzdZSUbGnJJZ3KJHIboTQSKVq8Xr7c4sAAfixbR1Ns/IhU266Ude8ahj9\nGMZAUdywK9i92LYPjyeFbRukUg25Cl8zSI0eKYVlhUbM+uY4PuLx+SiKRTDYjK4n89sBrvm4nIOb\nh2RyOqDlTL9hIFbSbhC/vzXnzGbn0qvOA1xP9qEx5O5YA4xes3rQ4Wz035jf304g0JU/dj/rEJYV\nJpWqz4WXOTiOhltbXM2Hww36AChKJanUzFzmtOJ4cFU1sSxfWQfA4aiq67U+VpSC399CINCJqtrY\ndgvJ5MyivX+PJ5kX2INoWobB2uiSY4v8xI8A6VTpXlsqOVR7KF/hyOfX+fTnzuDtt1pIxDLMX1jD\nwf19eYENcPBAlFd/u5f1V7madGwgTSZdfL902sSybCm0DxOvtxfYTWVllmw2nAtBmogm4TpNDc27\nPZSRqlCVqxvttgfDiJPN1uYTgpQ8UYCqWvmQJo/Hjdsu3C9ONluBbYexLF/eSxsgk3H3P6uqtuVe\n9AVisQW58DEHjyd6SF7v40EIPWdFGIpTEtdu2xq9vUuB8T3f4+kvKjiiab3Ytk4yOQfQcjnEB829\nfuLx0kVUboR5zRkgk6kgkZjDSN+LckVVHMdLMjkTw+giHC5U9HKd0NyF3NDCKDAj12dGLl95IdTP\ndbRTSSZr0bQMqppB09x64aUau1IyTo+nn0CgPSf8/SQSM3JbBYMhfzZ+fwfpdC2DCxTTDGDbRpHg\ntm3vmHv+kqODFNqHiRCCzDChraqwZHlhpXrG2bN59bd788VBBrny+uUsXFrPwqWFtv/xnddLnpEa\ncv8586uZs6CKA3sLiRnmNVaPOwOapDya5sYxg51LIZkE1Fxhh0PD9Y4eWbgahutE5KYZLbxUFWXk\n8pVuutCBEa5puYQlhcVeuYpXXu8Avb3zRnRWS6erCQYLucUzmUpsO4imJQmH9+HxpHAchUymKm+u\nPzqoJJPTCAZb0TRziIY9/gXDcO9zKE6iYpoVRKNj78n6fB1FudUDgS4cx0sqNa1s++He4eAm0vH5\nuggG9xc5pilK6d/JfU4/ECCdbsBxVCKR/cM86V1N3TCy+W0SN8+6VpSxLputpHirxM5ljcvmPo8M\nul4a9qeqForiDPGO10gkZuQWOZmcsC9fwERy9JFC+zB54/dNfLC9q+ic48C2d9pYudrd65o+q4JP\n/fEZPPmTLcTjWXw+nXMvbuTs8+ezf28vb7/RjKIonHXeXBYuref9bR2I3G/Z59dZtbogOFRV4RO3\nreGFZ3bQ251k2owwlw8pKCKZGF5vX8k+p7tHfOiU06TdPNqDmcUsNK0T2/YVmSHdYheJkr62rZNK\nNRQ5qA0iBPT1rSAcbgJGt1e73t9dw/ZgC6RSM7HtAB5PLDc21ykrEGjLOz+59+glm60gm514zoCx\nyGTqyGYr0fU4lhUaYaFRjKqm8HqjmGYI2y71GZiIlUDXk2WE/8im6VRqGh5PMm8RyWYjJJPTqKjY\nNWKIXSmFufp8/SPsd2eLLBGuRu8jkYigqhlsOzDEQc/FMPpKfCF03U32M9QM7zoBFouGTKY2l93O\nzH2OUmBPFlJoHyb79/aWPb97Rzd7PugiHHJfHitXz2Dl6hmkkibptMlzP3ufb3391255TsuV0Nve\naePWL3yIy65ZxgfbOtANjbPWzmHewuqie9c1hPjUH59xdCd2klEu3tlxRjb/eb29+P0dKIqJZQWJ\nx+fkBYtphnNpQAvatuN4i0zPbqhPK5qWJJmcRih0MCcsFTStWOi749AwzVBe+A+SzYZyzlC1Oa3J\nzo3Bh6qaZZyZRn/ZlkvSMnw/0114pMmOoybIoINTJlM1LsE7FCE8mGb5hDHD8fk6csU3bBzH9dJP\npytzZm2BZQVy2vrguNIEAq05M3EgXypzOOUEfTlteuiYo9HF6LobnuXu6SvD8o4Pti11QMtmgxjG\nbGDQI7y0n237clsWxWlQhShYhhTFJBzel4vlNkgkZmBZfoRQShaVQihkMmE0LYNt+4nHR7IuqWVL\ntkqOLVJoD0MIwY5tnbS3DLBy9QyqawM8/T/v8sH2TlRV4bQzZub3lwEqqsqHVMSiaf7171+lpi7I\nxZcv4qxz5wHgD3j4r+9tYse7HSV9+ntTbHz1AB//xCouumzxUZmfpDxu8Yy+fP7qQaercihKlmDw\nQN6s6GovCrHYYGU11zvazRWexbKCKEoWXe8suo+mWQQC3bksXYXzw1/mmpalsvI90uk6UqmGXC1n\nB9MM5UtAmmYV0aiRK0JhoesJVLXYe9myfLm9ykPDTexSsAA4jko2O7ZpuTgWuo1EYuYRix12twoO\nEgqZpNPVuee4C5TBePK+vuUkk1lU1cY0h3o6O0Qie/MhZ15vDFW1yuytuwVUdD2JYbhbE9lseETT\neAEFy4oUnclkKoq0djdrXBivdwBFEdi2gmUFc46IGwmFwsTj88hmI0WmftvWiEYb8yb3QW3b3bYo\nWD7C4ab8PvygI1l//1IymQheb3TYYkFnYGAxxQs6gabFAS1XiMTOhba5KWelA9rkMeFP/oEHHmDL\nli0oisLdd9/NqlWr8tcuuugipk2bhqa5msrf//3f09DQMGqf44VHf7CZt984gOPAS8/vonFxDVvf\nbstf/9UvPqCqJkAmY5FNW5xxzhya9vSyZ0gucRgssed6e//mlzs5/azZeAyNVNLkwAjaOYBjj172\nT3K0UIlGl1BXlyQeH8i9mMprhj5fb8k+oKYVm0yF8BR5iCuKhWHE0PVUmdjn4uPBnNRDk5qoahJN\na8ZxtPw+qOu96xao8Ptb8XoHcBwlZzotqMGOo6Kq04hGqw9Z2wVIJGahKDYeTzxXJaxuzFrgqpop\nEwvdkRMsh2daHawfDjZ+PyXFR9zn27n91zD2MGODxxMtiRF3zdnlQr50BgYWoWnuosXVcA99/KnU\nDITQMIwYjqOTTDbgOAFSKTfe3jT9VFbuys/D7+9BVS0GBhbiZkYbyIfOOY4fXY/n0tO6OI6KbbsK\nhKLY6Hrx1o6uJ9H1OLHYQlT1/bz3uevdXlk0J0UxiUT25LaHFLLZEJpm5X0CvN5uBgYWjlGhzskt\nWgezto210JGMlwkJ7Y0bN7J//34effRR9uzZw913382jjz5a1Oa73/0uwWDwkPpMNk17e3jnrYP5\n5CeJeJadO4btV9tuvexE3H0pvv5yEzd9bg2JuMmObR1Yps1brx4oqvTV05WktydBw/QIHkPDHzBI\nJko9zoNhL2vOHsmLVXL0UYBppFLDi20UU65ilON48Hj68fu7AZtstoJ0upBeUtPSudSeY49i8N7D\nTeFunefCF8vjSeH3tyOEXuRANrw2tdundsKe30LoxGKNlIt5HglNS5aJhTaZSCz0cNxF09D64U6u\n6Efhs7Es/4gLCyH0MqVC1VHGpYwracvoKKTT0/JpSAvjjGBZEUKhPWX2zmMj9vP5uoqc2HTdJhg8\ngGWFyGQiOU24+PM3jF4sK8zAQCOBgPt9yWZDJb4JgUDrkIppoiQKwuNJ54rKzB1xtuHwvqIqZq7A\nP23E9pLxMyGh/frrr7Nu3ToAGhsbiUajxONxQqGRv9gT6XOs6WyNl3h4izKK76DABujtSvLKi018\n8rY1LF05DccRHNwfpeVAIbdyw4wwtXXuPHVd5axz5/Drn+/ENN2iCbX1QeYsqObMD89h3sKj59wj\nOTJYVoR0uiYXAyywLINMpjKXZtTVwAf3s9PpaahqmkhkzwjJMoop1rBHvjaIm0O8uPjG8DZuHHAF\nYzmqjc34ha1pVkwgFnriZLOhnDd2KhfvPYORxmtZIbLZSrzefmBwT/dY/O7KLXpcM3S5gjCHimEk\nMIwEPl8n2WwkF0ude6IyqL3buTKuNqYZxLLqS8bj9Y5dMnS0cqFu9bjiSAf3eBxOEJIxmZDQ7u7u\nZvnygsdydXU1XV1dRQL4nnvuoaWlhTVr1nDnnXeOq085qqoC6PrRjwfs7koQH8gQDBlFQnnZygb6\n+9Ps29WDokBVdYDenmLTmmM61NWF88ef+uyZPP6fm2lrGaBhephrP3Eq06YX9gA33HIGa86aw/at\n7cxfWM2q1TNRxpuS6jhi6JxPJMY3r1VAFEig6/VEIvsZzNcN7ksyHE4QDoeBLoZWdhqZUiehoqtK\nECj2Lvd4MuUbo+FqWiqa1gDok/D3OgXYj+tUFcTrXUhd3ZGoWDUHGCoEDPz+eUBlvoV3TH+pU4E2\nIIWiVBEK1XB09Yf9QDvu36QC97NxgO0MdygbRNMqRvmbzcT9/pWr0ibw+RzAC2SGnHeKcpYbRoKa\nmk5g5ZDeHWXvWYyCz9eAzzfS2EqjLgYXoCf3O+PIcES8CcQwe9wdd9zBeeedR0VFBV/4whd4/vnn\nx+wzEn19Y2f+OVxamvv5wcMb6et2n+UxNKqq/cxZUMXHblyJ4dXZtrkVj6FjGBr//0Nvkkm7X2xF\ngdnzq4oqedXPCPFnXzmXZMJkztxqenriJZW+Kmv9rL3IdXzp7p5YaNFkUlcXLpnTicChzUvFzcqV\nwu+3S1766bQgFovh91sl1xxHLcls5TgCIdQis2c6XUU2G8FxvLlylR14vVFUNVNSSnIQyzKGFBZx\nsO12NG06XV3j+80dOTzA0FzoNqNlMBs/Krq+gKqqAVIpk3S6BsvSJnDvSO4fR2hc5dH1ASor9w1Z\nkHWSTIIQGsFgscB2w680kskQicS8UcZl4PHMw+vtRdMyJaGCpmniOD683uJiJsN1A9NM0N8/+AxB\nZeVuPMPcHgZf1e6+uY9Mppp0OlRmbIJgsDmfBXDos9LpCD6fId8Zh3DPkZiQ0K6vr6e7u+B41dnZ\nSV1dIdH+Nddck///+eefz86dO8fsM5m89uK+vMAGMLM2p6xo4OobCyvQU8+Ylf//xz+5ik2vNWNZ\nbrvz1zeW3FNRFIIhA1Wdehq05FARZLMhTNOXL95g2xqKYlFVtRUQRbGwjqMSi80jEGgvuourjTg4\njhtqZJphEonZRZ666fR00unpBALN6HpxBIKb27oOw+gvckRzTfbtwIlT1tXdr55OPH78CwHDGCix\noOh6smzomGlWoGmnkkiMPS+38EolYFNZ+X5R4RDTjJBK1eWc0hKAUrb299B4do9noGxO+0JWOYds\nNlCyv17oHy3JJeBuHdWQTE7HN5rfmmTcTGiDae3atXntefv27dTX1+fN3LFYjM9+9rNkc0Gcb731\nFosWLRq1z2STzZTuNWYyI5uIzjhnDn9651r+/K7zWXfFkgmZtvd80M2Lz++is/34f+lIRkZRTCoq\ndlJV9QG6nsY0DRKJaVhWEK83jq5n0XUzt4dokEzWE40uIputHrEWtqq6+7/x+PwRQ2tcD+6CDdhx\nNBIJN2d0+TrPcvE4WViWr8Q50LYNLCtYct7d9z9UNGKxBaRS1WQyYRKJ6SQSs3EcP9HoEvr6VpJO\nV5f0siwP8XhBGSkX2TCc4Wluh6LriTKJYDwjxsBLJsaENO3Vq1ezfPlyNmzYgKIo3HPPPTzxxBOE\nw2HWr1/P+eefz4033ojX62XZsmVceumlKIpS0ud4Ydmp09j2Titm1l2Jen16PpsZwP49vbz4/E4G\n+jPMmFPB1TeswPBOfGfhZ/+9lTdebsIyHX77y51ces1S1l64YOyOkqOEwDB6c5pI8UvT6+3Kl15M\np2tLtIxgsKUoiYquZ7HtRNnCH7qeJZkMYtsG4fAudD2O4yhFRToK7Uc3ZTuOj2h0UU6zEaTT1ViW\na1JLp+vweGJDymL60PVZSEegySGbrSGTGciFphUSvTiOL2fadrOeZTIVpFINE9pbt+0A8XjhHaLr\nA/h8bp3udLqGVGo6Hk8in9nONP1Eo4uLQgDT6epcwpmCRj7cpG5ZI5f6NM0wQrQVtS+XmU5yeChi\nvJvLk8Sx2gPZ+Mp+tv6hBVVVOP1Dszn9LHcFms1YPHjfS3R1FPad15wzm0/ctmZc9x2+39HTmeDB\n+18knSpo8g0zwtx5z0VTypR+ouxpK4pFJLJryJ5gkJ6e+TiOD8PoJRLZWxRKlUzW54pOuEQiH5SE\nxDgOucpYxXWOATKZMEJo+Hz9+XO2PRgWJvLPicfnFKU4PVR0PVFUFrO2tuaE+HsNZap9B90wOHNY\nohcY7lVeVxcgFtuXXygeamy9rsdy0QruO8a2dQYGFmBZIXy+7iHe8qXar2H0EgodyFcgs6xgvnzr\n0EppIxEIHMTn68lVVgsRi83Ll2Sdan+v8TIl9rRPRM46dy5nnVsad/jOppYigQ3QtHvk5Chj0dMd\nLxLYAIlYFttyUA1ZNedY4/N1DnPiSRAItBOPz8u9fApXBstkDhXa5WKfB6sxlQ/RipWYRDVNkEzW\n5Qs1ZDIVZDJjCWwHn68zl7zCnxPwhYdZVhDLGj3eXHJsse1ASaIXl+LEJvA24bD7zvH5uhgYWJjL\nSjY+fL6eouQ/mmbh8/UQj0fGXAhms9X09g43pY+vLClAMjmLZHJ6ruCILGJ0NJBCewxq6gJomoJt\nF960ukfFccSENOPGxXVMnxWh7WAhjnH2vEo8UmBPCuXiYwvnSt+wiuImsRjUYBOJ2RhGf1EM9tDY\n2NL+peeFcE2Yh5LAY2jyCre8Yyqf0lQydfH7OxkaMqXrWfz+jrIpVkfmSFvsSoW1olh4vT04jpGr\nJjb0mZos23kUkUJ7DBoX17Hi9Bls2dSSP9fRGuNf/vb33HDzaUyfVZqD2XEELz63kz0f9FBZ5efM\ntbOZv8jNuazpKtfffBq//vkH9PUmmTYjwlV/tOKYzUdSjGlGEKK7aA85m3VNU5blxzDiw4SsIBBw\nc4gbRn/O0WcZodABPJ74uBKoDMfVyMefvlZVMxhGIXmPW2+7f0haU8nUwQ2T8ngGAA3HKRWQqjpW\n3HQx6XQNhtFXZB6fSM75kdD1GOHwPnQ9ixCQzVbk061Kjj5SaI+DT33uDGbMivD80zvyOcUP7O3j\nqUff5U/vPLek/a9+/gG/emZH/njne53ccfcFRCpdp4y5C6r57B3nHJvBS0Ylm60iHp+Vc9oReDx1\nuZKUTq6YQ6GtEMVZyjyeNKFQE7HYImKxReh6nIqKnWVDa0ZDVV2zezweGbuxO5IyjmqCwt6oZKoQ\nCLTkF4FALv2xyqBJ2hVq9CIAACAASURBVBWK4/1euFhWiIGBhfnvtBvHfuSSfwQCHfnQMEUBrzeK\n19tzxIrBSEbnpBTaQghefXEv72/twGNonH3ePE5ZOXIMq6oqaLqaF9iD7N7Rzc/+eyvXfKK48Mmu\n94qrOfX3pXjr1f1cfMWSIzcJCa6WciBX7EElna7J5fs+NNLpBtLpegyjn4oKB01L4ji+UVM1DmIY\nA2haAtsOYlkhEomZuSpc2ZxgFbnQHpFPpFLObC7E+LUUx/GRzUbyVZxg0GJwUv6cpzRuDHUBd1FY\nSSZjoigil8P+0B0SLStEPD7xkFo3iU9fzmmtmkymkFNjaKGSwrhHDgWTHFlOyl/5ay/u4+lH380X\n9di3u4fb/9eHmTm7csQ+i5fV4fXp+Uxog7zy273Ma6zmtLMK8Y6Gt9RE6Q9OrFiDZGQCgVYCgUJB\nF01LYds+THPsspHDCYf34fW6DoaVlRqJxKyc52zB98DN4W0WCV1VFXi9/SSTrtPX4ALARaCqVj4e\nW9MSeL29GEY850lcuG8qdSiJhhyy2WDu5alimqFcrm3JVKP8QquagYGR30VHG6+3m1CoOf8993ji\nCKHn66ybZrDIedNxtNy+tuRYcFJGvO/Y1lFUhSsRy7L5zYOj9pkxu5KLLl9UUsQB4L++t4l///Yr\ntLe6L/izzp2LP1DwnJzbWMWZa+eUdpQcFqVaiija63VDtnZRUfEBPl8bI5mPdT2O11so76iqbu3g\nWGw+6XR1LtNYJalUefNfqQe5kvun5q65x7YdwjSDaFoq/z1yzZ8hHGe83sGCSGQX4XArhpFE11O5\nWNiT8qc85Ukmp+WT5LhlMiO4ecUnD8OIlixMh/6uXA/xOkzTRzYbIh6fnStZOhyHQOAgkcgHhEJ7\nGXSw83o7qajYRlXVO4TDuxg717lkKCelpl3OU9s7jmQpF1+2hLffaKajtTgETAjY9X43Tz2ylT/5\n0rmcduYsauqCbP1DK/UNYVadOQOPRzoIHWnKZRQbTA3pOsvsz5eHdE3oStkUjINxqENRFAshPMRi\nhYQVVVVbynp+a9oAqhoeo74weDx9VFTsKwkjc7WWkUpWCvz+NrzeKEIo2La3KJmLu8DolvuJUxTb\nDtLXtwyfrwfH8ZDNVlJXN7kLsHLav+MMfX+pJBIjl+UcJBhsLrKEQQbDaCAUOpBftOp6FE3bQX//\ncqQj2/g4KZfnZ18wj1CkkAJy+qwIH75wfCEVl35s6YjfrV3vd/Ovf/8ye3d2M3teFVdct5zLP758\nXAsCyaGTTDZgWa6gHNRSBk3TXm9/UT1nVziWT4CQyVRjWcXasuMMLxMlUJRyIWAQCPQRiewue30o\nwWDruOppD8Xr7SIYbMXjSWAY8ZLYcXcMh+6xLjme0Ein63Pm58kXXMlkcYpcy/KRSh3avrrX25nP\nyFYggd/fUWKt1PU0Hs9gsiGBrscwjB6G1keXFDgppcnipfX82Z1r2fRGM16vxoc/soDAOPecV66e\nyUfW9/HGy00lSVIA9nzQw4/+7U1u/fNzmLugNN+v5MjhOAH6+pbl4kX1XPEEJXet1LJRPic3gFLi\nCKYoWYq1XwXbDqBp5SuyeTxpAoEDJBKjLf5KzfPuYqOCkV7WbrGJoeNyC44M9VA/VO9iiWQ0HCdA\nf/8p+HxugSc3n/34LYU+X2cuq1q5e5fex81d8H/Ze+/wuOozf/s+bfqMumTJkmXZlrvBGEyJHSCU\nEFoSQg8hnfRfIAm53jdsCOxu2m7aJiT7bvJbsmlsQqiBkNBLKDbGGNxt2bKt3jW9nvb+cUYzGs1I\nluQmo7mvi8SnzjmaOef5luf5fKykTa+3FbvdknXVtF5CoaY8YRlFCeB09iHLCUxTSBvr1M8aMZdZ\nGbQBaup8XP6hFYffsQBXXLuScy9exP2/3cLeHf152yNhlTc3tFNZ7WagJ0JphbMonnLMEHMyW0dI\nJKqx2wMoipWxresKiUQZhYahRTGFLOdmv8pyMqM2lj1nCYoytm47i8MxTDJZWaC8xsDl6sY0jRyV\nNCtge4hGx893KDRUmUiUIopG2oSkmIRWZOrY7YM4HINpuVHLTW70c2GaCvF47RTPaiCKalpjvdB2\nH9FoA4qyO8d+1pobL8NuH8qR95XlOC5XD+HwwlHrIni9B3KOl+UhRFElFFo8xes9OZm1QftI8ZU6\n+OjnzuTf73yWoD9fY7q7M8SP/vkFQoEEldVuLrt6BaesKb5cjxemKRMILMHpHECWw8hyjJKSA2ia\nk2i0PpNhLooJSkpa8o43DCXHOlEUE7jdPRMOb1uZ5MN5QXvs3J5lvSmTSJQTj+e+LMcSi9WgKBFk\n2fqNqaqLWKxh1vQqihx9JCmKx9MxKt8jjmmKxGL1hzlyfGw2Py5XF7KcKCgQYxHB4ejHMGQkyarz\nthqu5YCAJOW/R8cqFlr+4fnD5lZjWp0Vz8WsnNM+WtjtMldcs5LyytzhG1+pg4HeCKGA9SMc7I/y\n3BN78+q8ixxrJOLxKhQliiyraU3wOG53FyND1W53J7KcKiCiouLztWZqtR2OwYIvi7HkD8Hr2O3+\nnDWiCPH4XOLxeRxuDtMwnAQCy4hE6gmH5xEILJ0VL6Yixw673Z+T7wGgKNFx9p4MBh7PIRQlgSBY\nvtuFbagMnM4BFCXrNmflmlhVN6lUCYaR+zyMtSodb4rLNKUJpr/eWRR72lNA1wye/Mtu2g4M4/ba\nOf/iRZx2Zj2nnjGXvTt62bm1F1ESmVPn4+H7tuYc6x+KkUpqOJzFF+7xxOnszjFPgJFscQ0Q0vKR\nuYwEcLs9BLRiGLaC4hGmac3RZS0w7TlCGIKgU1KyN+/zTdOSlpwspikRj+dnvRcpMh0KmdwUmmue\nLE5nd14jACAer0g3ELKN3UIlsyNomjU37XD0I4o6qZSPaDS39x+PWyJIox30LO3+cmB2TEEWg/YU\nePyBHbzy/IHMcnd7kNu+eT5Ol8KyU2pZvKKGN15rp78njLfEQTiY/WFV13qxO7J/bk3VefXFgwT9\ncRYvr2bpyqkreRU5PCNz2qMxTRHTlPH59h+292yzhTNBfLT3tWEIRKNzSaVK0lmyAvF4VcaGEKyE\nnEKfn0r50klzRYocfxKJSmy2QCbJUdNsR9QoVJR43rSRYYhEIo0IgobDka3x1nUZ05QyOSTZREwA\nE0mKI0kqomigKHFkOYamjVZ2E9Ke3jqCYPkEqGrZrBJ3KQbtKdDaMpizPDQQ5c2N7ay/YCGmafKH\nX77B9rd6AJBkAV+JHV03qZrj4f3XrkRI/7JN0+TXv3idlp1WEtuGFw9y6VXLOffiRcf3hmYF+U37\nVMqHIGjp2u0shaw0czO3TeLxMkxTxjSFtP2gNO5cYKFSLFW1F80VipxgREKhZmy2YURRI5ksP6Ip\nl0J6CZZnuEgkMg9oR1HiSJKDSKQKTXPicvWltRCsoC+KHWiaG6dzMPPMjSSihULNmfN6vQdy/Ot1\nXSMaLZv2tZ+MFIP2FChUb+31WXXCh/YPsWtbb2a9rplIssS1Hz2VwYEw1bXZ5KTd2/py9MlV1eDN\njR0Fg7Z/OMbmDe04nTbOWt9YzEKfIslkOYoSzgzRaZo9kylrzYFle9pWIJYxTXXchDPLBjOCzRYH\nwOkcIBRaUNCQIZksw+EYyAyfW8N4VRRTSYqceARSqYqjcqZYbA6yHE0ntFkypyM2saZpJxy2gm5V\nlZdUKpROWIsiCGqOLLCm2fKeO1FMjfp3Epstt+RSUSKIYqrgkP87lWLQLoCa0nn+yRb8gzHmzitl\n3QULEEWBs97dSG93KFOfvXhFNavSGeGhYDLHcxuseex779kAwKvPH+TDnzqdeQvKiUVTeYkaqVR+\nr6x17yD/e+/mTHb6lo0dfPZr64piLeNgtdwlRvdik8kKdF3G4fBjGFK65lRJbyvD6RxAEEi/bDzY\n7bFxA/aIkMpoJElN+x07cbs702VidmKxOnTdTTg8H6dzEEsv3DctQ5MiRWYyhuEgEFiG3T6MaYoT\nisS4XN243b0Ft0lSKmcKCsgpuTRNy7p0tP2tNdU1uxrBxbd/AX73y03s3tYHwOYNHQz0hvnQR1az\ndl0jc+b62P5WN6WlTqLRFH/41RuUljk575Jmqmo8DPQVFt8Y7I/y0jP7ufmzZ3Lq2rm89PR+erqy\nSVCLl+bXGr/6woGccrL2g35ee+EA73nf7KhHnCyiGMfrbU+7cynE4zU5tduaVkIkkm8iEo3OQ9dt\nuFy9SJI+rmKargtI0viZ/zZbkJKSXaOyYiNIUpJgcAmqWoaqzq7huyKzEXFSUrqKUvj9OEIiUZke\nGdNQVUvXfATTlNMN7cFMQzuZLJt17naz624nQXdHgH27B3LW7dzWx5WqTiqhUV7h4rKrVvDQH95m\nw0uHMvscah3ivPcu5MHfb2U8ImHrpa4oEjd95gxefHIfoWCSeU2lXHzl0rz94/F8C7xYNH/dbMfj\n6cgEXEnSEcUuUqmSnKSwwghpERWr5T6eglMq5cHpDOZvTCOKZlpBLYuiWE5ehY0UihSZnUykrKZp\njlFCQyaFppGi0UZU1YuixFBVF6nU7FOdLAbtMRgmmGPqqU3D4P7/2cLenf1gwuKV1bS15urqth8M\nUF7ppmF+KR2HAhRi/sLsD2xOnY8bPnn6hNeyaGkV+3ZlGxAut8KpZ5xYB6CZyIjwyAiSpGGzBQAR\nSUqQSnnRtLE9bTO9b352t6bJ6LoLw5CJRueiKDHs9lDOsN1Y8o1EpFnXAyhS5HDEYtXIcqhg1YYl\nJZyVDS6MNRd/tObjT0aKb5UxzG0oYeHSSlp2ZoOlr9TJ2290ZZa3vtGFw5n/p9u5tYePf+Es3tzY\nQWB4pAxCIB5LsXBJJe99f35veiIueF8zhm7QsmsAm13irHfPp75x9pQ2TBZdt2UUlsDqHdtsAez2\nUHoeuo9otJZ43Mo/cDp7sdsH07Wl+S8Pw5AxDAVZjuL1thGP1xCJNOJ09uXYao5lJPt8ZNgu33Sk\nSJHZig7sxuMJomkKgpDMe45Gz1UXxhjlTz97qy+KQTuNrhkM9kcor3Rz82fX8vzf9jE8FKOu3kdf\nd5jOttzes9dnzzMMUVMGoWCSVWvqkCWRJStrqKnxMTBQeK70cAiCwMVXLOXiK6YW7Gcb0WgdgtCO\noiTQdZlkshSnM+uGJQgmDscw8XgtshzG5eqasNcsSSlstpHeewJZjuP3ryCZrEQUE9jtwzgcfcjy\n6IQYiEZrEASrjnQ2DtsVKTIeHk87MISSrg7TdRHDMHOeQ1Ud31Pe4ejD6exHklKoqotIZN6Upp5k\nOZQxQEkmy09qnYRi0AZ2b+/jrw/uoK87TEWViwsuW8zlV2fNRJ59Ym/eMaeuncv2LT30dWcDss0u\n8crzrXS1WSbyC5ZU8vVvXXhc7mG2YbP50zZ/KTTNTTDYjCSl0HUHshxLZ2xnGamZttnCeQHbMEac\nhqzl0QpOYGWI2+3DCIKBLEfRdQXr0cm1/tR1J6lU0de6SJGxyHKuTKokGcTjFUhSMv0Mu9I13WMx\nUJQALld3pidus0XxeDoJBpdM8rPD+HwHMsqEdnuQUKjppA3csz5oG4bJU3/ZnQm+QwMxHrlvG5tf\na8fhUHB7bJx17nxOXTuX3dt6EYAlK6q58LIleL0OHvnjtsy5UkmdrjYrYck0oXXPIE/+ZTfrL1pw\nIm7tHYsgqHg87UiSlZQny9bQ+IgbkKr60DRXjhqZqnqx1JTseSIqowP2yPJorNrsIA5HcFRNqZJ3\nHre7C0EQ0wYIRYoUGWFsfsfIFNJEgVOWQ3g87TmSpSNIUr6s8Hg4HEM5UsKiaPkBFIP2SUoirjLY\nn1uGoGkGB/cNZ5a3vtlFaZmDefNLWX/hQlaeZs2NdrTl1uwWYnj4SIT4ixTCbvdnAvYIsjw6oUwg\nFGrC5epBklJomhNVdeF2d6CqTlIpb4486UTOXTAyR55ryTn2861rUHE6u0kmx69TLVJkNhKP12Cz\npYDUqICdX4Y5Gre7u2DABtD1kXwRnZKSfUhSHNOUSSQqMrkrWSb/LIpiDKdzKCOEZBiOSR97vJj1\nQdvpUqie46H9YOGMb7Dmqgf6Ygz0xQgFkyxZUYNikw4rciKIsGRZUUzjaKNpzrxe7lgpRcNwEoks\nQFECuN1dOSIqWa3jLIUkTEfW67qIouQH6UL7W8N96qxSaCpS5HBYgis1RCLtaJojHbAnDqajk0tH\nME3L+SsanQuYlJXtzIy0gY7b3Y2muXJ60fG4pbU+0tDWdRlVdePxHATMzBy3LEfw+Voz+zkcfgKB\nRRjG+HPtJ4JpB+3vfve7bN26FUEQuOOOOzjllFMy2zZu3MiPf/xjRFGkqamJ73znO7zxxhvceuut\nNDdbknaLFy/mzjvvPPI7OEIEQeDSq1bw+AM76O4YvxZ3hP7eCDu39bD6jHrWX7CAzRs6SCayQy+K\nTcRb4kCRRVatmcu69yygpyeIaZjYikpmR4Wx82NWqzi/BMTl6sTl6s0b+rbZQmnJ0uzctmFYy2Mz\nWq398wP2eEFe150FtZiLFClin5IxiaY5cwK3rksEgwvRdWuqy2YbzAvs1vM6nBO0dd1NINCM02lV\nBKmqB4+nY9Qcd4BwuCknsIPVaHA6B4hGG6dzs8eMaUWRTZs20dbWxv33309rayt33HEH999/f2b7\nt771LX73u98xZ84cvvzlL/Pyyy/jcDg488wz+dnPfnbULv5o0bysitu+eT4P/+FtNr7cdtj9taT1\nZVfN8fKVO8/n9798g1AgTuUcLxdetpimhRX85U/b2Lm1h6995mEScQ1REli8rJprP3Yasjy7ZPeO\nNnZ7MC8Qi+LYwGrgcAwVDKyjE9FGgu9EimeFKOQXrOsSkUg9xaHxIkWOnEikHkEwcpQOdd0HWGWb\nTmdvwee7kLOeYbgywdftbh8zx22kJVjzTza6YT9TmFbQ3rBhAxdddBEACxcuJBgMEolE8HgsC7WH\nH3448+/y8nL8fj+1tbVH6ZKPHNM0M45bI4iiwNU3r2Z+cwXtB/yYWKIqWzZ2kErlZhNHItkAUVnt\n4St3vidn+59+/SabN3Tkfe6bGzsor3RxyQeWHb2bmYUU1hrOXScIBoIwse2mtd/YNQ50XcvzwB6L\nNEbYSdcFAoElM24orUiRkxXDcBIMLsnzFJDl0GHLNqeOQCpVht0eyFSPWF4FMy+pdFpBe3BwkBUr\nsiVR5eXlDAwMZAL1yP/39/fz6quvcuutt9LS0sL+/fv53Oc+RzAY5Etf+hLr1q07CrcweV55/gCv\nv3yIRFxl/qIKrrl5dc68tCAInHHOPM44J1t6EI+pbN3cnVm2OyWWnTLxPHX7wfET1HZv7+WiK5Yg\nScXe9nRJJCrS7j5WCYiqOonHc7XbTVNOG4BkpzwMQwTMwzzsHiIRH17vobzSr7FD4qZpCbHouoNE\noroYsIsUOQaMzTy32SZWJxxtMlKIeLwqJ5lV1yXi8Qo0rYRw2MRut97fiUQFmuY7wqs/+hyVSVaz\nwFjh0NAQn/vc57jrrrsoKytj/vz5fOlLX+LSSy+lo6ODj370ozz99NPYbBMn7JSVuZDlI7ejbG0Z\n4MlHd2UEUfxDnZSWOfn4586e8LiPfe5sfvOfGzm4fwivz8EFlzaz8pSJpURLypz09xYWxu9sC3Lv\nTzfwlTsvwOU6uZOVqqry7SiPD16gBBgAZBRlLlVVhf6WK4EDQBxwIIrzgW6ggxEZU7ADyVH/rqek\npCz9GQNADNAAEUGIMFpBzRpW9yBJp3GYn/GM4MR9X8eOd+I9wWy8LwPruQwDDqARGC83pBQY6xQm\nYvXES3A4luBwTJT17QVcWO8CA0maQ1lZ6aht1jD6hKcYw/H8vqYVtKurqxkczIpX9Pf3U1WV7elE\nIhFuueUWbrvtNtavXw9ATU0Nl112GQDz5s2jsrKSvr4+GhoamAi/P39+YjpsePlgnoLZgZahSamV\nffTzZ5JMaMiKiCSJOcfs3dXPvl39VFS5OXNdI5Iscub6Rro7g0TD+dmPAC27B3j0T1sLmoScLFRV\neaet9HZ0kICRpJYk2cA7lpEGlonHsx+bLYAgmGkLQR+JRCVebxuCoKPrEjabO31fCpBbOlJauhNF\nieesi8dlIpET+XeYHCf++zr6vBPvCWbnfXk8h3IEkZLJAKHQYgrnh7jxeq2hbEEwUVUnoVAjpulI\n98rV9H8gy5bj3kgZps0WQBRVkskKTHP0lO30/97H4vuaqBEwraC9bt067rnnHm644QZ27txJdXV1\nZkgc4Pvf/z4f+9jHOPfcczPrHnvsMQYGBvjUpz7FwMAAQ0ND1NQcv3Ko6hpP3rqSsmxTyjBMnnps\nN3t39qPIEqefU8/Z5zZltmuawVOP7SYwHGfuvBLOv6SZl59t5cm/7EZTrd7X3p39fPwLZ3HamfU0\nLihj6+ZuujuCvLWpM++zw6HJiwMUOXKsxJXRKmkGTmcgPYdlrbHMQ7YAKwqcwSCVcuVoj2uaRCJR\nVECbCqZp4t+8F9UfpvrCNQhjkwPGQQ1GQBBQfEXXtHceBjZbbuWOzRaewCVPIBxeSCwWQRTVdPnY\n2OlGE6/3wKjA3o1pyihKFEEAp7OPcLgJTcuPCzOdaQXtNWvWsGLFCm644QYEQeCuu+7i4Ycfxuv1\nsn79eh599FHa2tp48MEHAbjiiiu4/PLLuf3223nuuedQVZW77777sEPjR5NT19aza1sv29/qQVMN\n5sz1csEoX+qXnt7Hc0+0ZJa7OizXrkVLq9i1rYcnHtrJQK9VarTtzW78Q3EO7h/KBGyA3dt6Obhv\nkKbmSsor3bznfc388sev5l2LIMKSldXH8G7fyei43Z3IchzDUIhGayc1lyzL8YLr880/4unEl9xH\nw+Npw+nMOrtpmo1gcObVcM5kjJTK5k//OwMvvIWZ0ig5rZk1v7wdd+P4jXdD1dh628/pf2ELCFBz\n0Rmc8qMvIh6FKbMiM5nRjl+F0XUP+jgeIzbbMHa7P5ODoii5o3GynMTp7CUcXnR0Lvc4Mu057dtv\nvz1neenS7FDvjh07Ch7zX//1X9P9uCNGFAVuumUt7QeGGR6OsfLUWmQl++Af2JdrtZlK6mzf0s3z\nT7awf/fg2NOxZ0df3m9K1828HnRJaf7EyKmn17Hi1JmTTX8y4fW24XBk1eokKUEgsIxC3ruj0fXC\nDcTC9dY6oqhitwfQNCea5snrCRRSRCsyMYd++yT9T72RWQ6+tY99//Fn6q5ch3/zXspOX0zVBWty\nKjv2//xhuh58MbPc+afn8TTXs+hLHzqel17kqGKmDXfi6LqDeLwmbfIzkHkWUykvup5tEFuGH0OA\nQCJRedgesiQlDqt0KIoTV4jMVGad2se8BeXMW5Cfxu9y57/UBweiBQM2WI2ApuZy3tyQHfqua/Dl\nBeP3vX85h1qHGey3hmWWrqrhxk+dcYR3AcmkxsvPthIJJViysoZlqyYvWnDyYqIouQl+ihJHUYKo\natmER8ZitchyDJstlHmYxxNIKS3diyiqiKKZFm4pZ2wLzcq9LFYAjGCaJvt/8gC9T29ClCRqP7Ce\nBZ+5kn0/+TPdj72GqeuItvzEoqFXttP14EuYKQ1BkWi65UqW3/XxzPZIS4GppT3t07tGXZ/0cHyR\nY4c1f53tJMlyjHB4QcbsxzDsxGLZ95miBPD5DmaqRWy2IMHgogldvlS1FMPoy6sAyd3HjdVA108q\nBcNZF7TH49yLF9J+wM9AnxUU5i8sp6LSxb5x9l95Wi2XXbUcj8dOZ0eQ0lInF1zWjDRGOKV5WTW3\n3Xk+b27soLTMyfJT5uTViE8VXTe496cbMqMDr7/cxuXXrGT9Be98YxLDEHNqpE1TmKQCmUQotBhJ\nimCzDSOKJg7HQMGgnZVFtIK63e4nlSpBkrJSt6paMiN1iY83kZZO9vz7/+LfvIdkT3YEJLD9ALH2\nPtp+83dMddQYpiiAka02SQ6GMFNWj8dUdTofeJHm265BKbF6Uq75+Y1Rd9PURql6n9rE/p8+SLxz\nAO+yRpb/yyfxLSnkKFXkWCMIGjZbrmS0zRZEFFUSicLTJHb7cCZggzXK5XAMEo3mB227fRCbLYBp\nimn50nBaWjg3eGuaHRAoL9+BIOhomptQaD6mac8750yjGLTTzG0o5dZ/Oo83Xm3D7lQ4/awGdm/v\n5Y3X2tE1c9R+JZx9biNnn9eEIAhced2qw57b4VBYd/7RC6hbN3flDOerqsGW1ztmQdC2hsYkqSvT\nC04mS9H17FCZzTactuxU0TQXiUQloqils0cldN1DPO5hRDFtdAnXeIiimTY48CDLsfSQ3mwY2ZgY\n0zTZ+tWf439jT/62pMrAC1tyAzbgXjgXARM9oVK6ZjF9T76es10NRdDC8UzQbv7y1YR3HWLgxbcR\nRJGq96xm4RevmvQ16rEku+76H2IHewBI9vnZ9c17OfuBf57q7RY5SuQ3lE2yJZhTPZeG292RNgwy\nkeXssLiuKwQCixFFNa0prqfXS8TjVXg8XRnFM5stjMfTmXEKnMkUg/YoHE6Fd1+UTUxYeVodl35w\nOVs2daKpOouXV/P+61YhiidWpjIRz59PHZ0Q904mkZiDprmw2cJomp1UKqs5bll2duRYdlrZo6Bp\nPWOyRUU0TcFmO3wWv6o60oYHxeHw0URbu/BvaRl3u628hCjdOesq169i5fc+A6aJIIpsuOZbDL2c\ntbctX7sMZ30Vpq6DKCI57az97R1E23oRBAHXvKlVnAy+si0TsEcI7TqEnkghOU6eIdF3CqYpk0r5\ncDiyAlSq6sMwxu/hJpPlOBz+TIC1lMqsqg2P5xAOR2GzJ6tHPkQsVk8otDDdSB8RZwrnSZROxe7z\nRFIM2uOQTGjs2dHHqjV1nH+JZXKSTGr0dAapqfOdUP3w08+Zx6svHMx4gIPl8T1b0DRfQaWiQpad\nI61uWU7icvUQnLyT4gAAIABJREFUCjWntxgFfbMFQSGR8GCaQjpD3UYsVksxYOdj6Dqy24EWKqCl\nIMC8T16KIEsMb9gJpknJ6kUs+j8fsqaH0n/81T/7Mnu/9wciB7pxN9Wx4IsfZPOn/43A5haUUg+N\nH3sf8z9xKe7G6Y1s+FY1oZR7UYezz4qjrhLRXjR1OVGEw03ougNJSqDr9vTzNT6KEhkTYHUEQcfK\ncTmc9bGJ09mLKKZIpUrTjW/SWg1jnQIlrB7/zPYOKAbtAux4q5vH/ryD4cEYTpfCu85vorzSxXNP\n7GV4KE71HC+XXb2clatPTAa43S5z82fX8tJT+4lGksxfWM75o8rXZiuGkf8gjkYUk+naTxd2+1C6\nDCSLptlRlLMJh09uD/TQnnb2//RBEn3DlKxoYuk/3XzUzp3o99Pzl1cJ7W2n728bCwdsABN2f+vX\nLL79BppuuQIjkaL2ynchKtYrR4smeP36uwnv7UD2Oln89RuZd+OFvH3rz+h9fIP1WT1D7Pnu7yk/\ncxne5Y103v88w5v34qqvYsFn34+lXjUxztpKmj59BQf/719R/WGc9VXZhkORE4RILDaxquRoxrr6\niaJlGqRpHgxDzmmoj37+Nc2GLEex2yPpbf1Eo3XE43Woqo9UyoeiZBNTbbYw5eVvYZpy2v6zfkbm\nrQhmIQ3SGcSJUAa653sv0XYgO3zjcMrYbBKhYPYlX9fg47ZvvuewQ+WzUd3oROF0duPxdI+73TCs\nBzqV8pJM+vD5unK2Ww95M8lkR2ZOPBptyKvZnskYKZWX3/d1wjsPZdbVX/8eLv7jN4/4++p+/FV2\nfvNekr3DedtEm4yRyi+hEWwypasX4V44FyOlkugdxrOonuHXdxLZM8pURxJZ8Nn30/6/z6IFcisE\nlnzjJrRwnNb/fCSTxFZ1wRouf+bfGRwsLBc8llhHP/4tLVRfsAbFO7Nr62fis3U0mO59eTwHcDpz\nf3OW0JGGaZqIojYqx8UaajdNEU1z4vMdymnEq6qDQGAlTmc3bnf3hGVhqZSXYHDJMbuvw51zPE6e\nt9FxwjBM/MO5QhyJuJYngTo0EGXvjj52bbPs4daua6Rhfm7Z0aH9Qzz9lz1ous66CxZQUjqxkH2R\nI0PT3Hk9bV23NIklSc8IqdjtYRQlnLev5c27E3t6ek1R4giCSTh88iT49T61KSdgAwxt3FXQH2A8\nRvYd2xs9+MvHCwZswArYAnn5RGZKw79pD/5N2WS14VcL6DjoBgf+89H89bKEZ2kje+7+n5ys88GX\ntzKwYRdCczYLvP/5LXQ//iqpwSCe5gbmf/IyXPWWvLKroRpXQzVDG3cS2NpKomuQZL8f77JGFn7x\nqqJYywwmFpuDosSQ5QSmaSWY2WzZER7DgFisklSqNK2OZv1uHY78oDwyzD6S6zIRshxDEFRMc2ZN\npRSD9hhEUaC23kcokMhZL8lCTha5okj88X/eJJa26dz5di8f/+JZmcC9/a1u/nTvmySTemb757++\nHo/38CUFG148yMaXDxGPqcxfVM7VH8l1IytiiTPYbKF0aUc1muZDVX3E49XY7UOIoo6qugmH5+N0\n9uFy5dbb5yuhFR5WV5QAufNcJjbbELKcIJUqnXEyiPbqMgSbgpnKDhnKbuekhoNN06TlB3+i+y+v\nYKRUqs5bzcrvfSYzpJ0cLJzwkz3BEV16QUpWNlFz8ens+uZ/536UqvPsVXeilHmpueRMEAVaf/EI\naFZCZv/Tm+n403P4ls1Dcjmov/FC+p96g65HXsZMjsp7eORlBl/dwarv3YLsdaGUeJCK890zCsNw\n4fcvx24fwjAU3O4eRrTFwXqWDUNBVUtzjrPZ8nu/qjr559U0hbQl6MyiGAkK8MEbTuHh+95m/55B\nRjooumYiSgKGbq2IjDEDCQYSvPFqGw3zy1BTOg/+/u1MwAbo6wmz4cWDhzUJ6TwU4ImHdpJIWD37\n4cEYdrvM1R9ZfRTv8OTG4ejD4+kcJVEYSXtZO4lG5xGLzUEU1bSikoAoJiec654IUTRwOruJx+di\n6Rm3ZlrpTmc/0Wg9icTMSQIsP3MZNRefQe8T1ryw6LRRf935hA/1oiEiu8cf7el+9BX2/eTPmR5t\n+++fRin1sOybHwXANb+W2MGx7krHFiOlIkoSVeevpv33T+dsSw4ESQ4EibR0opS4MwF7BHUoxNAr\nVq++/+nN1g+gkCPhS2/z4vovgSjinlfN/E9fQdOnLj92N1VkGogkk9aoia4PooxqV1m97/y5Z1nO\nzwa3BFWsUlGrJ22tN4z8hryVmDbzElCLQbsAVTUezj63iX1j1NBGAvZ4jIzevfTM/oIOX6o2jlDu\nKHZt680E7BG6O4Lj7D07Ga1qBiBJWqa0A8A0bRnZUlkOYbeHxw3Yo9/hhfYRBHC5+kgmS3A4hnOG\n1UTRwOEYnFFBWxAE1vzqdjr//DzRg724F9TSft+z7P3OH7BVl9L40Utovu3azP6B7a203vMIid5h\nou29OUPQAF0P/yMbtJvmwAuTvxbRZceIHVkZjZ4+fsk3biIVjND71w151wighifhBjjRFIFhgqET\nPdDD7n/5LUOv7qD09Gbqr7sAR1Xp+McVOe5Y6oaJ9HC5QDJZSiqVr3Kpafb0lJeFrovIchyncxem\nKaSVDq0yNNM0cbsHco43zZGSQANFCaNpzlHrThzFoD0OjQvKcHlsxCKF7TXH4vbaOONsy2Y0MJz/\nApFlkTPfVViFqbMtwI63uimvcFFRnZ8k4yugXz67yW/9jjeMNeLqUwjDEIhG60ilyigr24UgFK51\nF0WD0tIWJCl/uyDMPP1iUZaY9+GLAdh083cIpMVPEl2DtN7zMDWXnIlvWSN6IsXbX/wpkb3jy4Ia\niezvP9lVWNJ3PCSP84iDtqDIvHbNnQTe2JtzLXn7SSKmcfhG8WQwEil6n9hA7xMbaPn+H6n9wDpO\n/Y//U5z3niHouhu/fzk2mx/DsI87RRWLzUUU29PSqAqa5szRNzeMOMHgIjTNhyjGcTj8SFJanc+0\nEtEUxY/H04ksJ9OiLDXE43UFP+94MfP6/jOE0nIX5160cNJDqibQdtDKOG9qrsg77tS1dVTW5GcE\nvvFqG//1o1d49okW/vy7t3nr9S5OOaMOSbZOUF3r4T3va847bjYTj1ei69kXqKo6x+3tqqoHwyj8\nJVp+2rUYhoNkMr+lPppCAds6/+HLjk4k0THCIlokzuBLb2OkVF6/8V8mDNgAcrmPN2/5AW9/+Wf0\nvfT21D58PAumEQQBuXziv190XyfDL2+fMGCXrGmeUqLdVDBSKl0PvMihe584JucvMl1EUqmKCXNK\nNM1DILAMv38lw8MrgdzRNGukrB8Aw3ASDjeSTPpIpTxEo7XE47W43b2ZYXZJ0tNqiydWhKXY056A\niy5fQn93iC2bug67byyc4oW/t3Da2nrWnNXAM4/vZbA/W184krA2ltdfbsvJTN+7s4+Pff4s3n3h\nQoYHY5xyeh2KUmzhj0ZVSwkGF2O3+zFNkUSiOtPTttsH0glqMrFYNZrmJRabk37YsoIqpmkpMY0Q\niczDMETc7iSqmkSSEgWT1UbQNJlUqpxotP5Y3uoR415QS3Rf1nRDdNopOa2Zvf/2R4ZfK+zGN5rY\nvk5i+/JNOyaDOnSYMhgBznnoX3njpn8l0T008b5jjqtYvwpniZuqK9YRa+shuGU8l4AscpkXLRyD\nUdNUgiIjuezoiVRugtoYwns7xt1WZCYjZOa7DSM/3Nls4UyGuKqWjTEeMvMCtCTpyHKUVOrEaZQX\ne9qH4YZPnUFdfa761khttijl9uAi4RQH9g0SCiYIBXOzz1tbBgkGckvJDuwbpKcrlLPONCEYiNO0\nqILTz24oBuxx0HU3sVg98Xhdpo7a4ehNW3f6cToHKCnZjyCoxONzGR4+hXi8Ak2zo2l2YrHqMb1z\nkVhsHrCGQGAl0Wg9qupA02yoau70hGGIhMOLiEbnMdMfoaV3fITStUstow7AiCd5/bq76X483+f9\neFNyykK6HnxxagE7zdwPnc+imy+m+oLTSA5MLudD84dzAjaAqWqUnbmU9+37A/a6inGOBO/Shilf\nY5GZgyBomYTU0UiSjtfbitvdhiyPbWQK6Hpu4qauK+myshNHsad9GERR4LKrl/PCk/tJJFTOXj+f\n+YvKicdVnnx0FwdasnWrvlIHzcuqAAFZlkiNyh6XZYlQIM4/ntmPrpksXVXDQ394m+SYpLOKKhen\nn118QUyHsbWXspzE4RgkHq8FZKLRJqLRkafWxO3uQFGsF34yWYamOYEuSkpCiGIKQRBIJr1Eo3Mp\nKWlFkhIYhjIpP9+Zgm9pI8u/82lee9/XM+uMRIp4W98JvCpwL6xj1b99lu3f+NXUDzZh21fuAcBR\nV0HtB9Yj2m0Yycnln4ytJx94dgt9T79Jycom+sc0IGSfizmXnsX8TxazyU9mXK4u7PZ8pUPTJK2Y\nFsHhGCYcnp+WOjVxuboALZ1FbqDr9nQn4cR2pIpB+zC89UYnD/4uG1z/kWxl5Zo6autLuOLqlTx6\n/3Z6OkOUljs5/72LcHusYZNVa2p5/eW2zHmal1bym59vIpjugW96tS0nqAM4XTI3fPJ0HM5inejR\nY+x8trXs8bTjdGazRWU5W8pkG5Ug6nINpTXNrTltUdRxuboRRZVYbHTjaiQKzAx5zGhbLwf+v7+Q\nGg6jhqMFM66PJnOuejeDz25GC8cn3K/pCx9k8VevQ/G6SA4GCe9qG3df0WUHzcDAhAJqawCJ7iH6\nn93M0n+6ia6H/kHSHybROTDx/UpibnmYaRLr6GP5tz6BHk0Q3H4Ae3UZ9ddfQONNF2OryNe5nyyp\nQARRkSYstSty7JHlRN66sWWgoqjjcAySSpXhcnXgdvfn7C8ICWy2gbRj4Il7zotB+zBs2diR0xse\n6I2w8SWr3nregnJuue0cIqEUumaQSKgYhokoClz9kdXUN5YSGI5TWu7k9ZcPZQI2kBewAebOK6Np\n0fhDdEUmJpGoQJajiKL1wtY0J/F4JS5XFzabH1G0Ws2pVFlaNCXLRAmHo5PQBMFadrn6UFUfum7H\n4+lAlqMYho14fM5hk9qONXosyeaPfy8bEI/R+0Wu8GKvKqVszRJWfvfTPHvapw97jHfR3IyMaPt9\nzxRMMBNsMs23XUvzbdcgSBI9f9vIm5/4/rjnjB7soe0PzxJv68E0QCn15BiEOBuqiXekX8CigHdx\nQ05jwT6njPqrz8NRU845D3+blD+M7HFmRGUSfcO0/eZJTF2n4aaLJ2VeoseTvP3lnzL48nZEm8yc\ny89m5XduQZgoUaLIMcASRtJ1O5D9TVg5LQ5strHBfMSqM18eVxAsNUVV7SWRODG+EzDLg/bubb08\n9/cWgv44dfUlfOCGVZRX5hqrGwVa7IZhomkGf/7tFlp2DpBMqGi6gWlA48IybrplLeUVLs45r4mq\nKi/7W/r5y5+2553H6VaIR63kF6dL4ax3Nx6bG50lJJNVmKaMzRbEMKzyDJerD5erJxOUJUlDlnvT\nQ17TRxBAliM4nf3Y7cHMuUWxg1TKe0KlDzseeCG3B2sCspgnPjJpCoiSeJY1EtndhjYUJrqng+Db\n+9D8uS860W3HiGYTeVyNNdR9YH32tFLhAFZz0Rks/tr1meU57zuTkjOWENy8t/D16QbRlmyi2OiA\nDZAKR2m85XKGX9mBYJOZc/k5lK1dSs9jr6FF4qT8Ef5xwVdQyryUrl6UUYELbN1P+x+fpffxDaQG\nre/40L1/w1FXgb2ylIabLqL+mvMLXtLeH/yRnsdeyyy3/frv+FY00fiR9xa+hyJHFUFI4vG0oyix\nzJRWKuVBUSKYpkQyWUosNiddymm9gy3t8tL0vwv/NgXB0okoBu0TQDKh8cgftzE8aNVU+4fiGKbJ\np/7POTn7nbKmjta9gxm/alGE55/cy0vP7C/YW25r9fPMY3u4/hNrMuuGBqJoBV6Yl1+1HFUzCIeS\nnHJ6HfXziiIOR0oqVTbKfk/FZhsuoD9sPZSmqU9LJQ2sB1yWo8hybk2+JKnYbIGMetOJYKSHOBrF\n40QNTM29TLQp1Fyylp6/vjZmA8QO5SqjRVryM8zrrzkfI5EiuP0AjpoyFnzxKmRPdph43s3vpfP+\nF4iMCrjuhXUs/WauK5kgiqz/6/dp/99nOfjffyUydkj9MCP/eiBKzyMvkxq0kj5DW1uRfa4ch7JU\nMkhqMJjJtK9410p23fXrPBczLRIn0tJJpKWT4M6DuBfUUbYm32Evsi+/4iS0/cDEF1rkiJHlMHa7\nH5stOKpUS0UUe/D7lyGKKqYpZ/y7LZ/tAQRBJ5UqJZm0fLoTiWokKVaw1NMwTqzAyqwN2ru39WYC\n9ghd7cHM8PYIZ717PooisWNrDzvf6kHXTTBAn0DdzD+Ue97GheXUNvjo6chmitfW+zj7vKajdDdZ\nDMOkde8gJeVOqmtOjmSpY4HdPojb3ZXnrz2aQGABPt8BpHE63YWkDUfmwQQBHI4QhiGOOUackr7x\nsWDu1efR9rsnCb61H7CGm0W7DZha0F5469Us/tr1vHrlNzICLQC+VYsI7zmUu3MBt7vS1c3M+/BF\n457fVuLhzP+9k0P//QRJf5iKs5fTcOOFBXXSBUGg8aaLabzpYvb95AF6/7YRUQTv6mY67nsWU51Y\n5GYkYI8wrqUoENzaSvRgz4T7AGjBKL1PbCgYtF3z8nUDPM0zuzzwZMduH8bjaUMU89/NkqRitwfy\n9Bw0zUMkkv+8JpPlqKoDp7MbhyOYmXLTdYVYrObY3MAkmbVBu25eCXanTHJUjXRJqaOg1eaasxsw\nTJNtm8e3fRxNzdzcxBVJErnmI6t55vE9DA/FqKn1cuW1K4/sBgrQ0xni/t+8SWdbELtDZvUZc7nm\no6vfgd7BI5nfIUAgkaggkZiTs93p7J0wYJsmeDy9ab/cJKJoTqvXLQhGJrgbhoCuK7hcvSQSVScs\nw1yQRLzLm6wMcUFAsCkkeyZRViUKmQQuucyDZ1E9giBwyg+/wN7v3Uf0UDfuBXNZ9k83s/Nb9zLw\n3JbMoRVnLSfW2W9pk4sC1ReeTv117znsR7oaqln+z5+Y0v01f+Vamr9ybcYSMdrWx9ALb03pHBMh\n+1zokYkT6kawV5cVXL/46zcQ2d/F0Gs7rBGL966l8eOXHrVrLJKP3T5YMGCD9WyO6I5PFsNwEY0u\nIhq1EtQEwSSRqDzhVr2zNmhXz/Fy9vpGXn3hAJpmoigiK04df56iZJJSootXVHPZVcvz1jcuKOfT\nt75r2tc7GZ756x4629IlTAmNTa+1seyUGlaedmJl9442TmcPLlc2s1OWuzAM+6hhcQNRnDhgy7KK\nIGTnssYGbNOcnBPYSK/bOoeJoiRRlCQ2WyhtYnL8JWhbfvgnOu97ZsrHySXuzLy05o/Q8oM/MufS\ns/Atncfa334jZ981v7ydfT+6n1h7H57FDTR/5TpLPezBl3DUVVDz3rXHrbHoXVR/VIN2aOdBSlYt\nPOx+5eesoPGjlxTcZiv1cvb9dxPe14nktOGqnzn69O9cCs+TGIZIIlGJrk8taGeRSCRObO96NLM2\naAO4PDa0tN2mqhq8/sohzlzfWFDre9HSKuoafHSPGuIWRDDHTHnUzyuhtWWQrvYAi1dUT2hmPpaX\nn2tl84Z21JRB89JK3n/9KqRxknUKERjjA24altnIOy1oK0ruMK8VLMOZoG2aErruQpJys0Wt/2Q0\nzZ5TszmeUchUGLu/JKk4HIOoqhddt2EYx6/kJ/Bmy9QPUiT0SG4mbXR/F/GOfjyL5ubv7nWx/O7c\nHrJkV5j/iePfm1Q8R7dhZMRT+Dftzhl5GI1rQR31157Pwi98ACOl0f/8W5SuWYSztjJvX29xSPy4\nkUqVpBXOrGVdV4hG56CqvuP6/B1rZnXQ3rMjtw7PPxRn02ttXHTZkrx9BUGgpi43aI8N2AB7d/bz\n4lP7MAx44cl9DNwY5Yx1hxdL2bern789tAtVtYZ3+nvCOFwKl34wv9c+HjV1XtrT+ucAik2iefk7\nr4VvGPmZ2WPXhcMNaaH/GKKoZXrElljC1DK7DUPANM1x577Hw+HoxeXqTWellpNKWU5hYJBKlYwZ\n0j96KNOoK179i6+y/8f3E9mT1SJ3L5qLs2Fm/34GXnqb/hffRpAkzAm0zm2VJSilbqL7JzfFBYxb\n6x070M2+H/+Z1p8/jJ5MgWZgq/DR9Nn303zrNVO9hSJHiURiDqYpYreHMAzphE5RHUtmddBWlPxe\nrMM+/p8k6D/8PFdXe1ZSMZXUefGZfVTOcbFv1wC19T5WrK4tOGy4e0dfJmCPMDoAT4YrrllJPKZy\nqHUYl0vhzPWN78i671hsDrIcRVHiGTeeeDx3+MowXIRCi3E6u/F48l/UI+Ufk+lRq6qXVMqX4+F9\nOEyTTJC3ktaGsduHM0PuNlsY0xRJJo9+UPQubqDn8LtlWHLnR6n/wDpku8Ke7/2ByP4uPAtqWfz1\nG5HsM1foR0+p7LzzXiKjdcHHlLbJpV4abriARV+6Ci2epPWeh4h1DRLYvBctOIXEPCtpIbNoqhr6\nqOS31FCIg796nIYbL8BRfWLr9GczyWT1MXmmZhKzOmivXddI+0F/xrCjYX4pZ64fv1a6tt7HgZap\n6ST7h2L86ievkUrqCCKsPWce1318Td5+ZeX5lpyyJKCmdBTb5Lp4bo+Nj3/hLFRVR5LEgkl17wQM\nw0EgsAy7fRjDkFDVUsZTENE0d96ctWHYCIUW4XJZqmgjgbTQ3LauC4hiHLc7PO7c99h1hbLOsz39\n7LLd7j8mL5hY++QlShd99Tqav/QhwKqHrr5wDfGuQZx1FYi2mRuwAQZe35MbsCETsL0rmph71Xqa\nbrkSPaWy/au/YHjzHhSfm4YPX0T8UG9e0JY8jrwpghFK1iwiuHniaYfUYJDQ9oM4LiwG7dmBgSxH\ngeNrHiLdfffddx/XT5wisdgk9YSnQe1cHwuaK3C4FJatquGqD58yoYTowsWV9PeFCQzH0ScpC6ko\nIslEugdtwkB/hBWra/F4c7/ouQ0ltB8KMDxovUgkSaC/N8rWzV24PDZq6ycvUi9J4jFPAnK77cf0\nuzk8ArruSs9VTXSvJpIURxRVBMFSQYpGGzAMO6pamnEAUlU3opjv7DWigJabcJa/z0TL496BYBCP\nH/0h8kO/fdLK4p4IUWDezZew/O5P5PxWBEnEVupBmOpcwAnAV+Wl5TdPo8fyA63scbLmv76KaFPY\n8Y3/S9eDL6FH4qSGQvg37aHi3acQHjUVoJR7WfHdz+Btrkcp9xFv78XUjcy5Vv/sVgZf2mq5hI2D\n6LSx6vufK1gnPxVO/LN1bHgn3ZeiBCgpacXt7gP60oJbR28o3u0evyEwbU297373u1x//fXccMMN\nbNu2LWfba6+9xjXXXMP111/PL37xi0kdc6KYv6iC91+3iouuWIrTNXHRvM0uk0rqlqjKBDHb7bFR\n31jC6Wc3YBvTS04lddpah3jgd2/xm/98nZee3o9pmsiKxC23nsPNn12Lx2uz6sGBwf4oz/51b0Fx\nliIT43J1Ula2B7vdUkGKRucQCCzPebhSqXLC4YVEIk1oWn7S4GQC9PStnPX0f0eX0a5ZpgDJBXPp\nqW/CX1aFkb6Bxo+9j1N+8HnrKtK/rWAgzt8e3snD921l787+/BPPMJyVpTR9+gokT36SkZnSMt9L\neE+uGIseS+CaP4f6687PrFOHw2y79R48S+pxNVZjjNI61yJxOv/4HI65E081GfEUB3712PRvqMiM\nRxTjeL378PlaR+mZp3C5ehGE49MgmVaTcNOmTbS1tXH//ffT2trKHXfcwf3335/Z/u1vf5t7772X\nmpoaPvKRj3DJJZcwPDw84TEnA/6hGK17BnPWCaLVK9ZU6w3hcivc+MnTWbrKmmP906+3sHlDtkU/\nZ66XF57ax2Cf1WLf8VYP0WiKy65ajiAI1DWUEI3kfvmD/RGC/jgVVdMtWZh9iGIiPfRtBUVJ0nA6\n+5HlKMlkRUb5aDSRyDzs9gOYpvXdjFf2NZbxeuCTOc7t7iAanYcoptLKTBCPl+Fw+JHlJJrmIBar\nY6R9LYoJHI5BQCAer8I0Rzc0TSQpSdmahay8bSXx5Qv5xU/DaGp2O2b6xoZEuOVRRNFSHPN4bcSi\nKmrK+ntt+MdBFi6u5ObPnonbYyORUFEUaUrVDMeD5tuuoe6D63j9w/9KrDWbu1B5/mpE2WowO+oq\nYXRGvSjgWzaP0M5DOecyNZ093/49pac1531Ox5+eO6zyGsDBe5+g4pwVlJ81+QTSIkeGICRxuXoR\nRRVV9aTLs7IPo6IEsdlC6LqdRKKS6fdVTbzeQ9hs+bkQljxyFFU99mpp0wraGzZs4KKLLKWjhQsX\nEgwGiUQieDweOjo6KCkpobbWqnk+77zz2LBhA8PDw+Mec7IgKxKKIuX0eiVJzEicgqVIVlLmxDRN\nkkmNj3/+LERJoKczSGm5E5tN4s2NuZKPu7f1Zmq7yypcVNd66evOlitV1/ooLc/tTezfO8jr/ziI\nppmsPG0Op58971jc8kmLlTWe24sVRQO7PYyiRDGMfF9cq6b6TIaHh7HZhvF686U5x2MqCWoj+4qi\nics1iCiqKEoESbKu1+nsy+xjt4MsxwmFmpGkMCUl+zLSinb7IMHgEkRRw+nsTpe7mFz4+3WAyW2f\n7B4VsAEE61026mINw/qfoD93iNk0YP+eQX76nRcQBYGAP4FiEzlr/XyuOAbCQEeCe34t6x79Dvv+\n4wHi3UP4VjTRfFs2i3vxV64jdrCH0I6DSC4HtVeeQ+0V7+LQvX/LO1fKH8G9ML/EbTIBGyDVH2Dj\ndXez4PMfYOn/e9N0b6nIpDEoKWlFUayGtt0eyHHgczp7cbu7EIQRI5AQodBCpuOiI0mJvHLTEXTd\nhqZN3w0F8LNkAAAgAElEQVRuKkwraA8ODrJixYrMcnl5OQMDA3g8HgYGBigvL8/Z1tHRgd/vH/eY\nkwWvz86qNbVsejXbc7bbpZygnYhrvPJ8K33dYfp7w9TUerng0sVc97HTAPjpd17MO28iln2zSpLI\n5Vcv58lHdzPYH6Wy2sOlH1yW08Pp7gxy36/eIByytHX37OjFNOCMdxUD9wipVAm6bkOS8oesrOAd\nGMfMXsAwZGQ5ltd7Hr08mZ71ZOa/Aez2YF6S2mhstiCSFMLrbc/RQpZlldLSnQXV3CJhk0DgyO04\nhwezFROaZvDi0/uprvVOmLB5IrBXl7Hyu58puM23Yj7rn/wBAy+9jXNuFb5l1rU3ffpyhjfuzAnI\n7qY5NH/tehJdAwy8tBXDNNGGQgXPOx5GIsX+ex6i5/HXqHnvWpbd+dGiu9cxwmbzZwI2jBh6BDNB\n224fzARsa/8AshyeVoA1DAXDkDKNaxh5xl1EozXHzWf7qGSPm9OY1JvsMWVlLmR55iTFfP6r72b5\nqfvpbAvQ2FTGmxs7eHtz1hxAEODg/iH6eyxlqYP7h/nbI7t417kLkBUpLwENoLa+JEeE5bwLvbz7\n/EWEggl8JQ7EMUOSz/51byZgA6gpg5ZdA1z6gRUcT6YiHHNiWAy0AxEgNyfA6XTidBa6/ihVVS3p\nY3KxAmMZ4EQQdGDiLO3JJ6Qdfnt5uR/Ib4BIUuHnyOUW8HghMrV4Myk2/uMQl181M3rbU/kN1tyY\nK6ta9YlLiL7Vwr7//ht6IoWrvorT7/44tQ0V1D5wF6lQFESBpy/5fxjYsCtzXOmqBSz9/PsxNQ1H\ndRmdf91A633P5SY3aAbR/V0c2N9FxcJaVn4l2/MfeH03/l2HaLr2fJQC8/FTva+TiaN/X/lluIoi\npT8n/9kQBCgrU4DpXkcd0MmI5acgNAAL8PmOX6XOtIJ2dXU1g4PZud3+/n6qqqoKbuvr66O6uhpF\nUcY9ZiL8/olF+08EK0+rZeVp1vC/06PQ1RlkoDeCrIgsW1XD3h25L/OezhDb3uqioamM+vll7N6e\n3W6zSZyyto7/+O4LRMJJ5i0o471XLs30rIeG84djUql8cwRdNxgYCOetP1aM6D7PbBzAYrzeFhyO\nbPQyDBge9mGa+ddfVdVJoYA9QiTiJh6fg8+3F/uY9td05rUni2EM5JWNTYQoCnzyC+X87N+GJz20\nO1lUVec/f/QPBEHgjHc1MH9hBYdah+g8FGDV6XWUlB4f9anD/QYjLZ3s+bf7iB7swbNwLku/eXOe\nF/aiOz9O3ScuJ7ClheoL1iB7nHnnXPHDL9Ly738k2t6Hu2kOksvBnt8+hSCK+FbMp+mLV+Hf32P1\n2gvQ+dJWaj5yCaZpsvW2e+h+5GWMpMpb376PVT/4PFXvPmVK93Wycmzuy0FJiSfjf22aEI2WEI9b\nn+PxuHE6s1M/quokEMh6awuChtPZhyiqpFI+UqnR5XoGNlsAXXeg6yMluTXIshNFiaKqXjTNQ1WV\ncNTva6LGzbSC9rp167jnnnu44YYb2LlzJ9XV1Zlh7vr6eiKRCJ2dncyZM4cXXniBH/7wh/j9/nGP\nOZmZv7CCr975Hra+2UVljZuGxjJ+8i8v0DtqTrq03ElVrYdgII4gwMIllSTiKk6XwpqzGnjuiRYG\neq0f3b7dA6QSGh+44ZTxPpJ3ndfE9i3dDPRZAd3tsbF2fXFofDxGLPpGEEXLzD6RKCR/mSywzkJV\n7SQSVUhSFJst/yE9lkF7OqOrp5/t4r/vd/L4QwFeeiZJYHhiJ6zJMjwYpafTagTtfLuH+QvL2bW9\nF10zee5vLVx57UrWnH14FcBjiWmabP3az/FvstzJwjsPkRoKcvZD/5pXDumqr8JVP34HwrNoLmt+\ndTsAb992Dx1/yOq6+zfspPuhf7Dgix/EXl3K0IadpAYCOcePmIoMvPAWXQ+9hJkWUYod7OHALx7J\nC9pFpoJAKLQonQdiJaKlUtkk00ikMTPdZRgKsVgt2UQ0g9LS3Zn3g8MxSDSaJB6vRZbDeDxtKEoC\nwxBIJsuJROYDAprmO27z14WYVtBes2YNK1as4IYbbkAQBO666y4efvhhvF4vF198MXfffTdf+9rX\nALjssstoamqiqakp75h3CopN4oxzskHzgssW8/dHduEfilNa7uT8S5rp7w7z+1+9gX/IGs6pa/Dx\nkc+stYJvb27Pbt+egQk/r7TCxWe+so7XXjyAppqcdtZc5jXlCzqEggnsdhm7Y1Zr6OTNNZmmlThS\nGC+Qq0RnGJYqWiTSiGlK2GyBgsF5soH1WAb3sZ8Ti9fy7ovW8qEbrV7O6/84yAO/33pE583oDgDh\nUJLtb/XkLL/4VMsJD9rRgz34t+zLWed/s4VE7zDO2umrBA5v3JW3TvWH2fej+6m/5nzOffbHbPnc\nDxneuBtMk9LTmln45asBCO1uywTsERK9w9O+liIWpikTixVIHgRAzMxvj6WkZC+SlG2kjwgexeO1\nuFw9KIrVQxdFE4djKG3XOXm9jGPFtN/mt99+e87y0qVLM/9eu3ZtwXKusce8U1lzVgMrTq3lUOsQ\nq0+vJ55Q+eO9b2YCNkB3R4hXnz9AbX1+i802gZTqCGUVLi6/uvC8YigY5/5fb+FQ6zAOp8Kacxq4\n/EPHd757JhGPVyFJnYiinpY9LUVVx2spNxGPx1CUEKYpoqo+otF6QEQQVJxOq7RkOolohgGa5kIU\nU8jy0en1jveZui4Rjc4llcpVXDvr3CZsDoW//GkbkfCxqSsd6I9hmiaCIBAJJ9nxVjeNCypwOGV6\nu8MsWlqJohzbPBV7hQ97uY9kf7YBZqv0ofgmLpvUY0m6Hn0Ze0UJ1e89I69XrngLD/0b8RTtv38a\nQ9U45+Fv0/vUGxgpldrLz8mUntVe8S4O/OIRUqMS2zxL57H/Zw+hRRM46ivp+/vrEEvgW7OYJXfc\nnDm2yJFi4nD0I0lxdN2JqrpRlGiBZ9jKfRHF3GdDEKyKlJM6aM9G+npCvL2pC2+JnTPXNSJP8OKx\nO2SWrKjB43UQT6gkEvlWkcNDMS6+cimLl1XRstvqXdvsEmuPIAs8EVd57M872bvLOl8yqfOPp/ez\noLmCZauOjUHFTCeZrELTXJn5KWvearyIK6aHwcycfWQ5gtd7AFm2HmbDEIDDe3CPBFHj/2fvvOPj\nKM99/53Zvqveu9Vc5C73boMNxrRQQ00h4RJSTuEkuXCTnJyc3OQkhxOSnJObCoQUuoMBYzsGF3Dv\ntmRZvfeulVbby8z9Y6WVRruSCzYW9n4/Hz5YM+/MzmyZ532f8nsksFrT8XpjMJnqUKm8V2S17XJF\n4XSmDMm3hv5+Fi7JYO6CND58v5qu9kFsVhder0xzg9kvHPQxUasEBEHgzPEWtm0+x0C/E3Fom88r\nkZgSwd0Pz2VawZXTiNZER5D50Hrqfr8VyelGZdCR9chNqE3jdwSzlDZw+snnsFb5pVH16fGk3LqC\n3Cdux5jl113IfHA9gzWtSPbQYRTzyUoEUSR2wVR6DpbgMQ+iS4wBwDQlmZn//iUa/rgDV+8AUbNy\nsFY00f72Af/Bo7qKdR8pQwZmfv+Ll+cNuc6JiKjHYBjxarhckSElid1uv1H2+YyBlbb/bxUuV8wn\ncq3nI2y0L5Czp9t4669FAeGT4pNtPPHPK1CpL8wnOrUgibKzHYrOYGdPt5KUGsGX/mEZR/Y3MGB2\nMHNuCrnTgoU/zockyWx5uYhzRR3YbcoHis8n01jbd90abQCfz4TDcTHiNMpftMHQGTDY4HeZud0G\nVCrHhN2/RmqywWRqRxRbQxprv8tegyB4LrqbmCT5XYQuVyQ2Ww4XIh6hUotsuE3ZzW7v36vYsSXY\n/XuxZOfHI8sy+z6oYaDf/+CTfDLDGXHdHVb2bK+6okYbYMZ3HiVp/QL6jpUTv2oOsQumTTi+5tdb\nAgYbwNnaS8Pz79G99xTL3/kR+qQ4sr98G1Fzc6l/6e+Yj1fgbFYqx2liIql/YRvVv9iMu2cAbWIM\n0771ANlf9Lcszbh/HRn3rwOg6hdv0rnj6MjBY6SRQ7niw1w8guBBpxtQbFOrbXi9+lGqZuD1GrDb\n/a1UrdZMQBrSdFDjcCRNmvaeYaN9gRzdV69QKqut7OHU0eYLrlddeUMObpeXD7ZWBMRZPC6Jw3vr\niYkx4vNKrN6Qd8mZt4c/quPogcaQ+0QRMrInxyzx04ogBLuzPZ4oBgfzMBo7EUU3Xq9m6CEgIIo+\nRf0ooKixHovXq8flisVkGokPn69BiSz7k+MslgJk+eP/lNdtnEp/n51jBxoDMroXS0ycjk13z0SW\n/TkV42Hu/WSqQuKWzrxgdbKxCWTD2GrbaPzT++R9/S6aX92NLMO8Z7+KyqTn9BM/o33bYZBkNHGR\nZD68npohgz18zsr/fBXDlBQ6dxyl98BZRKOOjPvW4bNO3DVQE/3pT9T9JNFqe9Hp+pFlFQ5HUiDj\n21+nrfztCYKAxZKJwdCDSuXG4zEOhcH8Py5Z1jA4mM9Yj9tkIGy0LxCnM/ihPbpW+nwIgsCCpZl8\nsLVCsX2g38lrfzwFwP7dtdz76DxmzUsN7D99tJnjhxpxu3xMnZnIxjsLQnbvGs7mHYspQkvh0gzF\nOcNcPB5P5JDimP9vSRJxu2OQJD1Wa/DETavtJSqq/oJd4D6fLqTIyrCRlmWGOpqZhsbIOJ2JY0pU\nPh6iKHDPI/OZNjOJP/3m+CWdY3DAjdfnw+X0kJ4ZjaU/tOFOSb962bfjEV04jZ79oXsi9BwqoeGl\nHXj6/FUDza/sYtFfvsuC33+T9h0rsdW2knbHSmSg5Fu/URzr6RvkxMM/VKykK2teZfr3Pocq0oBv\nMNh4axNjyH5s0+W7uWscna6byMgmhfKZ2TwDWdYiSVo8nijFattfrhXN4OD5YtSTy2BD2GhfMHnT\nEmiqG0lqiY7Vs2j5xWXIRsfqyZgSQ0Nt6IxRS7+TQ3vrAga2sa6PLa8WB1qHNtWb0WhVbLh1etCx\nicnBs/LFK7O4/b7ZmCKuvB7utY7DkYogSGi1/gQ1pzMhZIORYdzuOBwOGzqdeUiIRUYUQ69eJUnA\nZssiMrI+aJ/TGYcsq/F4TOeJxV8+UtIu3aD6fDK//a9DaDQiqZnRZOfF0lCrzMYXBLjx1mB976vN\n9G8/iHfQRvv2o7i7zIH6dtGgxTzGVT1Y0UT98+8x+0ePk3b7isB22ecjalYOlpI65cnHuL4ll5ua\nX272G2xBQBMXSeT0LFw9A+iMWvK/+3mS1sy7Ivd5LeL/nY28xyqVm8jIemy2DHw+ExZLLkZj2xg9\n/2EkDIYuRNGNyxVzVcu5LoTrujXnxZA/IxFpSPEoPSuaTXfPJD3r/C7nxto+9u6sZNDiJjU9iozs\nGPp77UiSjCnC36RhNDqDhuVrcwA4uKeWmjENSkRRZGGIyUJmdiw9XVb6eu0IgsC0mUl89guFGE1X\nxmBfS232RjP+fQl4PFE4nYm4XAmjxBbGQ8DjicbpTMThSMLjMQ21CPUiSWokSUCWBbxeI3Z7xtAE\nQEajGQw8fNxuE4OD+UMtRI18HIN9MZ9XW0s/J480n3/gOEg+GY9HwtxjJ39GAoMWlyLBLS7ByC2f\nmfmxm49cru9g+/Yj1P1uK+YTFeR94x5mfPshjDmpCCoVprw07PUdyN7gBD1jbioDRTWUfu9FGv/8\nd1wdfcSvnkvU9CxsjR042nonbAEnOVyKfzuau/D0WnB0mBmsaCLzofUIk6xBy8fhSj4zdLq+ID0G\ntdqNTteHIPjweGLweKKHyraiGPktyURHV2Ew9KDR+CfZsqzC673w/JcrcV8TteYMr7QvEFEU2HTX\nxXXueenXRyktGulrXHyyhS//w3Ie/yf/zHzA7OC/f7xPEfubkjvi7jSFkDw1mkL3+1apRR59YjF9\nvXYkn0RCUjgeNhnwZ3Cr8Hhi6e+PQRTdSJKGUMliLlcikqRFqx0Yisslhxx3pSk53X7+QRdIX4+D\npauz2b+7BrfLh16vZvm6HDTayVHK1PCnv1P2by8hOf0P3Z79xazY+hMy7l1Lxr1rMZ+spPP9E0HH\nCVo1gihQ86stAcM8WNWCPiOJKY/exIp3fszJx5+l473DI8doVP46bbUKTYQBT//4ynuWkjo6dh4j\n7Y6Vl/mOr01crjg0mkFEURm7FkUJvb4bhyNpTEc8P1ptHxqNVTHeZGpGqzXjcKRMihKvsYSN9hWi\no22A0uIOxbaKkk6a6s1k5cQiSTI6vZp7PzePg3vqsFndTMmN5c7PjtRer1yXQ2lROw01fnd6XLyB\n1evzFOdsbe5n744q+s1O0jOjuO2+2eguoM47zNVAQJLGn0EDeDzRV/1BMVEC2cUSl2DklrsKmD0/\nldqqbqbPSp5U8ey2dw4GDDaA5Vw9za/tJufx2wGIWTiN2EXTMR8rD4zRxEcx9Z/vp+9EuXIl7ZMw\nHy9jyqM3IXl96JNj0cRF+nXNs5KZ/swjSA4n2vhoOnedpOH5bSPH+isIR/7UqNDFT573abLjcsUj\nSWp0ul70+rGuch8qlQuvN9hoi6IvKO9EFGV0ukFUKhdm8yxgckwwhwk/3a8AkiRz5KOGIM1nWYaB\nfgcnDlnYt6sGS7+TtMxo7np4LimpUUNjZM6eaqO1qZ/s/Die/OYqTh1pwmHzsHBFFpFRIw99r1fi\n9ZdO097sT0JrrO2jo93KzbdPI296okIYwuvxcfp4CyqVwPzFGZOuL3KYyUNmdmzQalsUh9p4XgSR\n0TpuutMvupSRHTMpKxhkX/BNSaNc4YIgsOB336TqZ2/gaOlCZdAhe300vfwBtvpgj4S9pRtnRy/1\nL2yn4YXtge2evkGiZ2UH6r3jlhTgHbDRe6QUlUmPPi2Bnn1FMHQ9UbNziFs+OZqyfFoYnvCqVJUK\nmWGv14DXG9rz6HTGYzB0KUq/hvG71824XBdfgnslCce0LzM9XVb++KtjCmnHYXR6NZvuLuCVP5yk\nt9uOxyPR12Onr9vGgmWZVJd38/Lvj3NgTx311b2cOdbCoQ9rycqNY93GqUEr6NLidg7uUSa8mHvt\nnDzaTENtH3MWpKFWi/T12Pj9Lw5xdH8j5860U13exez5aWg/hovy+otpf7q5mPuakheHw+7BbnMT\nEaVj+docTBFaOtvHd+eGwu3y0ddjZ86CtJAVDx+Xy/FZefqt9BwqCSSKRUzNYM5/PolKNxKG0kQa\nSdm4hOi5eVT/YjOWc/V+VbMQBt/R3EXTq7sZKK5Fco3kq/hsTgbKGsi4by2CKCJq1EQUTMHZ3guC\ngM/uxNk8Il8se7wk37xk3NW2paKJkmd+T92v36H3eDlxi2dMKBwzGfikflseTwSi6Br6twmbLRNJ\n0iGKDvT6LtRqB17vcI6IiMcTAfgQRZciWVSSBOz21JBu9dGEY9qfcj7YWkFjXXB2uMGg5u5H5lJ5\nrgvLgDJhoqNtkJ3vlvPhzip8XuXy3GH38t6b50hMimDmPKU4SkysAbVGVPTzBkCG6rJu9r1fzc13\nFvDR+zW0NY+UhDXWmtm3q4Zb7764GH2Y6wNRFLjrIWUTC6fTg9V6hKa6/oDOwIVQVtzBkY/qkGVo\na7EQl2Dkho1TJ01MO+9rd6GJiaBnXxHqaBO5T9yJJjJ0kmHrlv2B+uuJ8FpC16D3HTpH+Y/+ysx/\n+yKS18eZJ59joKgm5Fh3r4XWLfuY8cwjQftkn4/if/qfwLH9RdV4zIMsefl757226wFJ0jM4qKxO\n0Gj6iYxsQKXyV+LodGYGBqYBIj6fEas1F52uB5OpBZXKiywLOJ3x+HwXI8j0yRA22peZvhCiEQuX\nZ/LZLxTS12PnndfPBupuh4mJM3DycFOQwR7NmePNQUY7MzuWOYWpnDneGvKYfrO//tM6GFxPbhtV\nYy5J8lDbx8lXkxhmcrB3RxV1VZfW3OLYwUY6Wkfcla2N/Tz2jWWX69I+NlkPbyDr4Q3nHXc5xE56\nD5YA0LXr5LgGexhtXOiSQvPp6qBjzaeq8FodqMfpz329YzB0BQw2gFZrRa/vwekcUeVzuRKG6rnN\neDyGSVv6FTbal5nk1MhA4hiARiOycFkmoiiw+c9nqKvuVYw3RWjQalUhDetoxjPnD315EQVzUzh+\nsFFRHiYII5noudPiOXuqLbBPpRLIL0jE65V46+Uiaiq6UatVFC5J5+Y7Cy7yjsNcD4xNqrxQRBVY\nzMp4YWVpF51tFpI/Rj341WDKF26hfesh+s9Un3/wOLh6B+jeV4QmNhJBq0Z2jxJtUokBl3vskhlM\n+dzGkOcwpMahjjIqVvTaGBOiLnRlSRgQhGDvkCB4EQQXRmMHKpUHWRYQBAlRdKPTyYCI2x011EFs\n8ixowkb7MnPrPbMYtLhoqOlFb9CwdkM+02YmYe61U1/bGzTeZvVQVTZxK06NVgxyVw4jin6ltfmL\nM3j39bNUnOtEFAXmLEgLSKyuvCGXQYuL8rOdiCLMXZRO4ZIMdmwp48ShpsC5du+oIiU9irkLx2tz\nF+Z65VIfWetvnc7R/Q2KbTLyRSe1TQbUJj3Lt/yI4m/+mvb3DiN7Lr5Tm7O1h+OP/pi8b9xN4g2F\ndA2VkwlaDdlfvhV1hJH4rARi71yNSh86lmrISCL93rU0/vUD8PpQGXRkPLwBURN+nI+Hx2NAo7Eq\nJIB9Pg3R0bUBueFQssH+fcIErT8/ecKf8mXGFKHlS99YhsPu8atCpcXQ3T1Ie5tl/OXyEMNfGINR\nQ3yiiQGzk4hILXc+MAejUYskyeMm9IiiwN0Pz0Me8ruPdnULgr/GfGydeVuLUmtZ8snUVfWGjXaY\nIGYVptLRNnj+gaMwGNTcdPsM+nsdnDg8MjnMn5EYsiXtpwGVUYeru/+SDPYwsttD6+aPWLP3l7S8\nuRd7Uyfxq+aQcvMSABIT/b3PJ2L2T54gecMi+otrSFg3n7iFwSqJYUYjBkkE6/U9iv4A40UHdbq+\nsNG+HjAYla6qk4eaJhJHAkbi3Habh7gEme89uxFBgPc2n2PzX4qQZZmCucl85oG54xrvi4lLh2pO\nEpd4PqWvMNcjt3ymgMhIHTveLrvg9p0Oh5ei4y3c97n5xCeZaB9KRBvbXezThs82caOPC8E7aEeW\nJOKXz6Zrz2k6dh6n4cUdzHjmYRJvXnje4wVBIGnDQpI2nH9sGBR126MJtboei0rlIiKibqhl79Uv\nlQ0b7U8Il+viZubtLRb6++yUl3Syf1dtYPuhvfXExhlZt/Hjazevv2067S0WmurNqFQC02cns2Jd\n7sc+b5hrD0EQWLU+j/hEE3t3VlFffWFJad2dVro6raxen4dOf208bkxTM+k/HTqurY6LwNsXXBoX\nMz+f/lHJY7GLC9BEmzj+6I/oP+FvIuRo7KSk30r+ht9emQu/jnG54tHp+gLJaJIk4nQmIQg9gZru\n8Qy4IIDB0IdW24/XG4nDkYDHE/tJXr6Ca+NXNAnxeSV2vF1GW/MAWq2K2HhjUNb4aFRqQZE9HhNn\nJDJaT3O9OWhsS2PoFoIXS1y8kW88s4bK0i4MRjXZefGX5bxhri0++qCa4hOt+LwS02cn88BjC/jp\nd3af9zidTkXRiVZ2baskJs7Auo35rLox77zHTXZm/8fjdL5/HO9oGVKViCk3jSmP3ULZd14IOiZ2\n2UyMeek4mjuJyM+g4F8/j7O9F8vZWsW4geJaBqpbIW7yCdF8mvF6/U1D9PoeBEHG5YrF7Y7D7Y4e\naq3rwePRoVa70Ot7EEMsqFUqCZVqALXaysCA9qqVg4WN9hXivc3nOLh3RPgkJt7ArMJUzo1RmtLr\n1dx270wkGfZsr8Iy4CQqRs8Nt+Sj0aiIjgt2Ybe3DPDK8yeZuyiNOYVpQfvH0t/nQBQFomKCxRdE\nUaBgTvIl3GGY64Gzp1r5+9tlgQllW4sFQfBr3fvOU6/tcvno7vQbtv4+B9v+VsqJQ03EJZjYcPs0\n0jM/XYbJ0dpN/Ys78NmcTPn8zbS9ewhHY6d/p0/CVt1C/+lqDBmJOFqUyaXmExWs2vGsYpvP4UJl\nMihEWNSRRkxp8fQ7LywEEebC8XqjsFrH5lKoFfFqtboxpMEejUrlQ6/vw2YLG+1rirGlXf29DjJC\nPKTiEo0sH3JJz1uUTl11L3nT4jFF+BVxbtg4laY6M7WV3X73jSjQ2W6ls91K2dkO+BIhDbckydgG\nXfzt5SKqyroRBZgxN4WHvrQQtXrib2Vfj42SM+1kZseQO3VySfiFuXJY+p2cONyETq8mMzuGpJQI\nqsq7g/QD9u2auL54PLweidamAVqbBujptvLP31mH6jzfxauNLEnIkoyn38qxB/4da3ULAOpoE0k3\nLRox2kNYztWR+fAGqp59TbE9ZK6JKAa7Y0VhqLNX2GhfDfxtdM+PvxHQ1SFstK8QekPwWztzXjJt\nLQP09YxkLI42ihGROuYuUBpgvUHDE0+toK6ql/27axRdw1xOL8XHWwNG+/ihRqpKuxjod9DfZ2dw\nwIV31AO3+EQr6RnR3HjrtHGv+9jBBna8VYbN6katEVm6agp3Pxzu63utU1/TyyvPn6S/byTJKjJK\nR3JasKDIRCJAF0p7s4WK0s5A7/jJSPUvNtPy1j58diea6IiAwQbwDtiw13eMdO4aQp8US/yKWco6\narVIyh0rxp4eV5cZz6BSjMk7YMPW0g2xny4vxLWC2x2jaDgiSSBJKlSqkcYiXq8WhyPxql1j2Ghf\nIZavzaaj1RLolz19VhKLVkwhMSWSA7tqsFrdZOXGsekz5xczEQSBvOkJlBYF65mLagGH3cObfzlD\nyem285aVdXWOX0oiyzKH9vo7joF/ZXTySDMrbsghOfXTWaIT5vzIsszbr51VGGyAQYsLj8dHRlYU\nLU2WcY6+NDRaFbFxk7dSoXPXSap+/iay2//7dbb2BI1RRxtJu2cNbe8cRHZ50KcnIBr1HHvwh4rO\nYam1uh4AACAASURBVDHz88l78jOKY5te3U3Ta3sQVKLiJxs1K5vIvDR6+0JLoYa5srjdcQwO+tDp\n/LlE/pafVtTq0Z+/gCxfPQ9R2GhfIQqXZJKSFk1tRQ8ancii5VmIokBOfjw5+ZeW8LVsTTbnzrQH\npFJNkTqSUyP5+Q/3Yu69sDKUlAlUqGQZrINK4XuX00tn22DYaF/D7N9dS1tTaE1tp8PL4lXZrNCI\nvPvmOVyOS69PHs28RemkZU6+XsXD9B4qCRjsYUStBmlom6DTkLJpGckbFpLx2Ruw17XTX1RN8yvB\nCXrOzn5kWQ64yPuOlVH2/RfxDo78ZkWjjuiZOUx/5hFE1eTQZb9ecbkScbtjMRja0WoH0GiUCx21\n2oVO14fLdXVW22GjfQVJzYhibmH6eYUSLpSk1EgefnwhO94uQ6dXs/7Wqex8u+K8BlsQwGjUUDA3\nhdUbxs/eFUWBzCkxlPaPuOATUyIomJsy7jFhPn0c2F3DiSPNuJ1e8qYnBJLFQmGK0DJrXgoxcUa6\nO6x8+EHNxN4cYVRr6HHGzSlM5YEvFn6MO7jymHKD80QS1sxFGx+Nz+4kYloGLa/toex7L2BITyD3\na3fRf6oq5Lm0MRGKmHbHzuMKgw0Qu3A6yzb/e1j/f1IgExVVq2jvqdgrgyRdPcnYsNGe5Jh77RQd\nb0EQBaJjdWx/q5z+PgeCCGq1ioH+iQ22WiPy8OML8XokfF4JSZIZPZF3OjyUFXeQlRtHQpKJez83\nD41WRVuLhZg4AzdumoZGE575XytUl3ezY0s5nqE4bE+XjdiE0G5qg0nNzXfOIGbIjX3rvbPInhrP\n9rdK6RqnTafRqOHhxxeRkRXN26+VUF3eFQgRDeNyeSe9ccp4aD3dH56ha88pJJeH6MKpzPzR40Tk\n+GPwR+75Hv2n/UbaVtdO1bOvIWhDPE4FgcxHlM1IdInB8WpdYsykf0+uF9Rqa9DqenQNt9vt79t9\ntQgb7UlM0fEW3nqlGIfdE7RPluDcmTam5MXR3WlT7BNVIPn8ZWZ33j+bo/saqC73Z5/v31XLo19Z\nRHJqFMUnW9n2t3OYex3ojWpWrstl090zefSJxZ/ULYb5hKks7QwY7GG0WhWiKCBJyqXx7ffNZumq\n7MDfgiAwa14qM2Yl8/2nduByBrvK7TYPVWVdzJidzOe+spgDe2p59/USxZjI6Mnd97lz1wmqn3sT\ne3MXEdMyyXjwRnIe24QwNNuVJQlrTZviGFdXf2iBdkHGOCWZsh/+CWd7LzGF05jyhY10fnCCviOl\nABizU8n9yh1X+rbCnAe1ehCDoSvQi3s0Pp8alysBn0+HyxXP1WwgEjbak5iDe+tCGuxhZBkiIrUk\np0bQ1WENCLeo1SrW3zmNtTdP5eCeWkVDkvZWCx/urGbeonR2vFUacK077V4Of1TPklVTiE+8fPWH\n9TW97Hu/BsuAk/QpMdxx3yy0uvDX7krh80rs2FJKfU0feqOGFetymD1/JEM7LsSqOj0zmrU35bHt\nb6XYbR5UaoGpMxJxO320t1hIzYhClmVKTrfRWGcmNSOK6bMSOXsqODESQKNVU1PZw5GP6nE5PSSl\nRNDVaQUZUtMjL4ua35XC53RT9oM/Yavxt7t19wzQbtSR8+XbAmMEUcSUk4Krc0QVTtSqkdwh4v0S\nnHj4/wZCBW1bDuBo7WbZ335I2zsHcJsHyXzgRjRRk69v8/WEILiJiqpDpfI/b0eLYMmyP0HNbs+4\nSlen5JKenh6Ph2eeeYa2tjZUKhU/+clPyMzMVIzZsWMHf/zjHxFFkeXLl/PUU0+xZcsW/vu//5us\nrCwAVqxYwVe/+tWPfxfXKH09tgn3q1QC584Et0x0u3wUn2xDkmR6u4OzUMuKOzh1pDlInc1h99DS\n2H/ZjLbL5eXNP50OeAKa6s143F4efCysl3yl2PF2KftGyd62NQ+Qmh5FYqK/N/PSVdlUnOuioqQD\nSfIb0RtumUpqRjQLlmbyzut+l3Z1eQ8V57rQ6dWs2ZCHx+1j3+4a5CE9lcKlGajU4Btjp3Q6kfSs\nKF75wwkGR/VsX7o6i+z8BAoXp6OexOGW/qLqgMEeZqCkDne/FV3sSH/r6c88Qun3XsByrh5DRiL6\ntATMx8tDn3TM76zz/RPM/MFjZNy3bmjV3oosyWhjPn6/7jCXhl7fEzDYoJQz9fl02GyTw2DDJRrt\nbdu2ERUVxXPPPcfBgwd57rnn+OUvfxnY73A4+NnPfsbWrVsxmUx89rOf5Y47/O6fW2+9laeffvry\nXP01TFtzf6D0KhRanWrCxg1tzQO0NQ9gMKgD7vJhxsYYh4lLMDJj9uVTRys63hLkuh/dazzM5Wfs\n+2u1uDhzvIUZM/3JhCq1yGNfX0p5SQc2q5vCxRkBI3r6eAsnDzfhHaV05nJ6OfRRHSqVGDDYAOVn\nO4iKMWDuUeZUeH0yf/3diaAJYfnZTu77XOGkj9tGTs9ClxyLq3NEPliyuzj9+LMs/st3UZv8rv34\n5bNY/cHPsNa24bU7afzL+1irm/GYx0/qCyAINL+2m6ZXdmMpb0SyORF1GuJXz2HJy/96pW4tzATI\n8vimUKVyIYpuJGlyhHUuyWgfOXKEu+66C/Cvlr/zne8o9hsMBrZu3UpEhH/mGBMTQ3//5dHLvl4o\nOtmKzxc6/ValEsidGk/Fua7znsfh8KLViugjNej0GtQqgfbW4KxIUYT5i9ODmjq0twxw8kgzKpXA\nirU5xMQr3auSJPP+1gqqS7vQaEUWrchi8Up/H++4BCOiSkAadR9ju5+FuTx4PT7e31pBT1ewdyZ6\nlHxtT7eVt185S0tTP1HRemyDbnw+mZhYPefOtCkM9jB2qwedXrk6drt9RETqgsaOJ7xiGXBRVdbF\n9FmTWzJXGxtJ7lc/Q+VPX1XUWvceLKHud+8y7ZsPBLYJKhWevkFOfeVnuDr8kyVNXCSiXourrTfo\n3MNETM3g3HeeR3KMnF9yeejefZqz3/wNG/76DJ5BO31HSolZNB1dXLjc8vIgo9X2oVK5cLlikaQR\niWinMwGdrhetNvj3I0mqq6qANpZLMto9PT3ExcUBIIoigiDgdrvRakeatg8b7MrKSlpbW5k3bx5N\nTU0cP36cL3/5y3i9Xp5++mlmzpwZ8jWuB47ur+fkkWa8bh/TZiVxy10zAy03IyKCH4gGo4bktEjm\nFKaSlRtLfU2fIhkoIysanyTT3qIUwnC7JTweF6vX5xOfaOSV508GTQgkCTrHZATXVfXw8h9OYBnw\nuznPnmrjiadWKuKiH+6sYs/2ysDfLU0DxCeZyJ2aQP6MRGbPT+XsKX/Sjt6oZtna7Et4p8Kcj81/\nKeLU0eag7UmpEVgGnFgGnABsfeMclaX+yZ5t0M22v5UGxkZEBX/nwF+6KAjQ1jzyvcrJjyc5LZKe\nrvoLvsaqsm7OnmrDZnWRk5/AmpvyJuXKO++rd9F7sISu3acU250dwYa48eUPAgYbwNM3SPp96xg4\nW4O1qkUxVlCrSL9/HbLPpzDYo+nafZLqv3zAye++iLO1B11yLPn/dJ8iph7mUpCJjKxFp+sf6trV\nic2WNZRUBiAyMDANg6ELrdaMRmMPNHhyueKQ5cmz2Div0d68eTObN29WbCsuLlb8LY/TuqqhoYFv\nfetbPPfcc2g0GubNm0dcXBzr1q3jzJkzPP3007z33nsTvn5srBG1evLMci6F4XjiaEqL23lvc2nA\n6LY0DRAfb+L2++YAcMe9c6go6aSq3J9EFpdg5Cv/tJIZc0Zqpp02Hwf21OByeSmYk8I9D83jP777\nQchrkGWoq+rls59bgH3Qw/vvlStijgAqUVBc65ZXigMGG/zlQWeOtfDgFxcG7qu1USnK4XJ6qavs\nZemKHACe+u6NHDvYQGf7IAuWZpKVfWEt7Sz9Dna+V47D5mb+4kzmLfzkmtCH+rwmMx6Pj7rqYMWu\nmDgDXe1W/v52OScPNfPo/1pETUV3iDP4sVpc6A1qnEMCKiqVSP70eBKSIjm8f6T5TVyCkX94ei3R\nMQaa6800N5zfi2aK0FB5rpOONr+X59yZDpDhvkc/Xr32lfqsMm6crzDaglpF9s2Lgl5PE+LZF5US\nw01v/AlHRx/9ZY00bz+Kz+Fiyr1rSN+wkCPf+J9xX1cXbaLk2dcDCmyuTjMNv3uXwq/fiSZy8irI\nXShX77fVC4x8T1UqH1FRvUD2mHEx+JMQeoABBCESozEJo3HiyeUneV/nNdr3338/999/v2LbM888\nQ3d3NzNmzMDj8SDLsmKVDdDR0cHXv/51nn32WQoK/FKdeXl55OX5xT0KCwvp6+vD5/OhmkAByGz+\ndMv5JSZGhhRXOXKgPqhkZufWcs4WtTF9VjKr1+fypX9YxonDTditbhatyCI61qA4V8G8ZArmJVNV\n1sWxAw18/5vb6Qjh+h7G7fay7e0SZi1IIW9GPL997hCWfv8KTKUSyC9IUJzfOugMOkfxqRaWrplC\nTq5/bKiGD6JKUJwnvyCB/AK/xvqFCM24nF5+/ez+wMru4Id13PXQXJYMud2vJON9XpMZn09CHNOa\nSBRRyJJ2d1l58ddHJsyDAFi4PBO1WkStVrFiXQ6R0Xqe/dfdinh2X6+dX/7Hh6SkR03YIzsqRodK\nFDFGaklJjeTUUeXK89C+OtZuzL+IO1VyJT+r1C/eSnZ9J917TyOo1aTduYKom5YEvV7M+oWI244E\nXOmauEjiNi2jp8eK1+mjvaSB2JuXErvQr/ff3T1IyiM307zzBLY6ZdkYAiRsWEjjn99XbLY1ddF4\nsobo2TlX5F4/Ka7mb0uv7ydyjF31eNz09493Pfqh/wAmzlO4Evc10STgktzjK1euZOfOnaxevZoP\nP/yQpUuXBo357ne/yw9+8ANmzZoV2Pb888+TmprK7bffTlVVFXFxcRMa7GuZiEht0LZBi4vKc11U\nlXWBLLPmpnyWr534h9rWMsCrL5wMkh8di0Yj0lRvprayh8goHbfcXcCj/2sRR/Y14HZ7mTk3haWr\nsxXHzC5Mo6KkC7d75EHf1mzhz789zvf/cxMAa27KpaneTG+3PxaUmBzBihs+3sPl6IEGhSvW7fJx\n+mjLJ2K0P42oVCLzFqfx4c7qgHGNjTcGVQ7YrOOXD4I/YzY+MYLCJelERumRZZmKcx1YB8fUrcr+\nhLeJkgoFAb7y1EqSh2Rzt/3tXNAY23m+s1cTQaVi9o8eH3e/x2LD3Wch/e7V4JNo33YYUaMm48Eb\niV00g+6Pijj7v3+Lo7ETUach7e41zP3F12l4YTu9h88RPTePhHXzEQ1aPL0WJLeXhNVzyHhwPYOn\nqug+NpKJHjEji8jpmeNeS5jz43LFYTB0oFaPfOe83tDZ+qJoJyKiDVF0BTLHJ0sSGlyi0b711ls5\nfPgwDz30EFqtlp/+9KcA/OEPf2Dx4sXExMRw8uRJ/ud/RtxAX/ziF7njjjv49re/zeuvv47X6+XH\nP/7x5bmLTyGr1udRca6L+urgOJksQXlJJ2tuOv8q5NTR5gkNtkYjkpMfT1NDH06H3/gOWlzs31XL\nv3z/BnKnjd96c8HSTCRJ4q2/FuPxjCy16qt7OXemjdSsKCQZ4pOMmHttSBL0dFl597USHnhswXmv\nfTxC9WmWpIl7N1/vbLprJqnpUTTUmklIMpGWEc1Lvz4acHUDGE0aBgeChSOGkWXY+kYJH/69ig23\nT6eloZ9Tx5oViYQXiiz71deGjXaopDWtdnK35RyPiv98lYY/78RrHiRm4XTm/fzrZNy/joHSBlpe\n30PvgRK6D5wNtO2UXB5at+wDWaJl80cwJGJjyktn1fv/FeT2Xvzzr3LkX36LtaYVdYQRXXwU577z\nPNlfvo2oGVmf9O1eE8iymsHBbIzGTkTRg8djwmYLNRGSiYxsDCSkaTQORNHLwMCMT/aCJ+CSjPZw\nbfZYnnjiicC/x8a9h/nrX/96KS95zaHTqXnyX1ZSdKKF4pNt/t7Yo9DqLswDoQ1R8yqK/sQyAI9H\norHejMupdItazE5cTi9GU/CKfzQ5efEhs9gPfVSHZcBBRWmX4qEuy1B8qpU1N+WRmnFpUn9LVk3h\n+MHGQCa0KMKssP75hAiCQOGSTAqXjDyI7nl4HscPNeLx+ChclIlWr2LrmyUKQx6KQYuLPdsqsTs8\nl2Swh/GOmugVLsngow+qsVpGJpjTJnkmeSha3zlAzS82B9Q3+k9UUPHjl8n7x3s49aX/VCSljUZ2\ne+k5dC5gsAFsta00v7qb3K/cqRibvHwWK979D+pf2E75//0zjsYOeg+do3t/Mau2/TSkDGqY8+P1\nRmGxTJyJr1I50WiUGeRqtR1B8EyaZLSwNNVVRKUWWbg8i1nzU/nNfx0IuIRNEVqWjXFVj8eKG3I5\ne7qNzqEEH6NJi90W3KlrLEkppgkN9oDZwaGP6qgo6QqStwS/C3s83C4fvd32SzbaEZE6Pv/VJRzc\nXYvT4WXqrESWr/l0x/OuBguWZbJgmd+ID8fd8mck8NtnD2A2B+crjMZiGX9FPhqVWsTnlRQTRYCE\nJBNLVo+EM6JjDdz10DwO7anDOugkIzuWux6cc/E3dZWpHmWwh7GU1tH88q5xDTaAaNKhi4/G2aJM\nBBR1oQ2BIAh0fnAC2TUS0nA0dHDw9mdY/KdniCrIvvSbCBMSna4bgyG4jFaS1J/+kq8wlxe9QcOT\n31zF4Y/qcDq8FC7NID3zwmbTkVE6nvzmSg59WI/H7aNwcTqvvXQ6YMTHo7XZwou/OsrDjy/AYNBS\nW9lDdUU3GVnRZGbH8vufH6Kr4wKEIkKQmh7JjDnKVdTxQ41UnO1Ea1Cxcl0umefJIk/LiOazX7x0\nF3uYEfb+vYqS02047B6mFiRijNKd12inZkThcnrp6wmdCKo3qLnroblkZcdQXtLJlNw42lssVFd0\nozdqWH1jXtCkcP6idOYv+uSqAK4Esjc4kU9l1CN5JvZeSDYXHqsdQatGHiV32r71EFkPb0DUBhtv\nURUcPnA0dFD6nRdY/vaPLuHqw4yHKDoxmVpQqZSfrySJOJ0JwOQJ5YSN9iTBaNKy4bZLi5tERum5\n5TMFgb833VXAznfL6WofRKNTh+yB7PVKlJ/tYNd7lRhNWvZsr8TjkRBESE2PuiSDrdWpmFqQyIbb\npqMelVV++MM63n2zJCC8UVvRy9f+9ypi465eCUtZcQdVZZ1ExRhYdWPux9JD7+22cujDeuqre3E6\nvOgNamYXprL+1umX8Yovjp4uK309dlxOD+9vLQ+89z1dNoQJnj+iCFm5sWy6exY+n8RH79dQX92D\nx63MKdDp1Sxa7o+vJg31Ws/Oj2f5umvbIxI9J1cpcyoKTPvWg6gMWjp2HsM3OH7XPXttG9qkGNxd\nI6VHvYfO0fzmh0x59Oag8Wl3r6bn8DnFahvAUt6A1+pAHWEIOibMpaHTmYMMts+nZmBgKj7f5NKF\nDxvta5DZhWkUzE3hT785RvnZzgnHdnVY6e6wBhLNZImQq/Sx7s+xTMmN5cHHFpCYElyqcK6oXaGU\nZe61c/xAIxtHTTQ+SfbvqmHH22WBmGtlWRdfeWplQNjmYhi0OHnhv48G9aRubeonMkrPklWfbMa7\nLMu8/epZTh1twuX0YTSpg1TK5Ak+Rxl/w5nEZBNR0QamFSTx/tZydr1XqRjnsHn482+OcecDc4iN\n//TXD18oBd//Am6zFfOpCrTREWR9fiNpd64EIOXWZbS+8eGEx4822MOMlkwdTeYDN4IoUvwvv4JR\nFRyGtARUxtBCOGEuDa/XqGi/ObxtshlsCBvta5b+Pge1lUrBjVB65W1N/UExb59PxmBU47D7t6s1\nIrlT4xXdwvznU5OSGknh0nRWbxg/0z2Umy9UffcnxaljzYokqdqKHs4VtTF3wYjr1uuVqK/uJSkl\ngujY8Vc0R/Y1BBls8E9waip6Lslo11R0s++DGmxWN1k5sdx+36wJm2z4vBJnTrTg88lERuk4sq8+\nEHa12yZ2245FHrru99+t4P7P+4VPVKrgyYzb7aPkTDter8SX/3H5Rb3GpxlDWgLL3vg33H0WVEY9\nKv1ICMDedH5Z4bFoE2NIv3fN+PujTaj1Orxuf5hCNOrI+/rdCOLkcddeC3g8UTid8eh0fYiijNer\nw26fnMmvYaN9jeL1+PD5lEsqjUZkzoI0io63BDLCLQOuIAOalRPLprsLOH6gEbfHx6z5qSQmmWiq\nN49kHgtw0x3TueEC2iwuWp5JdXlXYMWnN6hZvPLqla74PMFLzdEZ1bVVPbz+0mnMPXb0BjUr1uVw\n6z2zgo4BxtWHB39C4cVis7p4+fkTgSzrpnozXp/EfY/ODxrb2W6h+GQbxSdbA96RqGhdULOOS6G9\ndYDTx1qYOS85pJ75MC2N/Xi9kiIccj2gHaMH3nOwhIHT1Rd3jsQY5v3865iyU8cdU/vbd/BaRuUV\n+CQMmUkX9TphLgQBqzUHhyMJtdqJyxUDTJ7ks9GEjfY1SnJaFHnTEhSr46kFSTz0pYU0N/TT1T7i\nAvd5JTKzYxBFgdh4IzffMYOk1EimFigfDnc/PI8Th5rweLwsWJzJihsvLH6pVokKF63T4eXgnjpu\nu9dvCIuOt1BZ2oXBpGHNBn8Ck8PuISpGf0W0qafNSgrIaQKkpEdRuGSk9d4rz5/A0u8KXOv+XbXM\nX5xOWojkwGWrpnD6aDPmXmXCVnpWNGtvzrvoa3vtxVOKsijwd9Qay74PavhgW0VQvoJlgjrsi6G1\n0S/aExWtJXd64rjjIqN1IVfi1zKO1m4qf/oq9oYOTPlpzPjO52h+bQ+S6+LEYky5acQUTjzpdXUo\nXeeSy8NASR1xS65OaOlax+czTUqX+GjCRvsa5tEnFrN7WyW9PXaSUyO56Q5/YlR0jF5htEURbr6z\ngII5E9fNLlyWycIxJUQXwq7tlUHb6mv9ojIH99ay7W+lAXf1mWMtqDQiNoubjOwY7vvcPJJTL2+X\no9vvm43BqKGxrg+fV2LGrGTMPXbamgfInBIbMNjDeL0S7/2tlLkL0liycorCMxGbYOQLX13C4Y/q\ncTo9GPQaMnNiWbg8E41GhdfjY9tbpTQ3mImI1LHu5nxypoYWtNm/uyZk5zaPWxnS8Hp8HP6oPmSC\nIfhFS9zuixej0WpFZPz11SOeGDdlRe0kpUbQ02VDkmQE/FVPpkgtq9dPzqYfVwpZljnz9V/Sd8Tf\naKXveDnOjj7Ul6ALbj5WxqE7/g+L/vx/iJoe2vMUPTdXIXeqS40j7TOrLu3iw1wThI32NYzRpOXO\nB4JrYddsyKOrw8qA2YGoEpi3MJ0Zs6+cy81mDV79mYdKiYpPtiniy6ObmNRX97JjSzmPfT1YJncs\nDrubt145S1NdHyaTlhU35ARahI5FFAXW3TyVl/7fUWoqeqgu72HbW6XIcmjlLoDqsm6qy7opLerg\ny/+4TGGoMqbE8NkvhG588c5rZzl6oDHwd0frIE/96zr0huASn9Ki9pDniE804XZ5KTndRkJyBPGJ\nppC68MMsXjmF6vJuujqsgU5FEyGqBG65cwbLb8jh1JFm3nmtRLHf7ZboGuoAl5IeyR33z6a708bs\n+SnEXMUKgMuNpbIZV7eZhOWzEMaRV7bVtWE+UaHY1neikln//hhdu0/hs01cSjcWe307DS9uZ+6z\nXw25f9aPH0eWZQbO1qJLiCXnyTvQJVya/kGYa4Ow0b4OKZibwr98P5Yzx1tISolkakHiRa2WXE5v\nSMEVj9vHWy8X0VDbh06vZtnqbJavy0Fv0ALKh5k49HrjdYgbJlSSVyi2vnmOouP+hhR9PXb+9nIx\nu7dXYorQsXT1lCBd9QN7awMd1PzX4f+/ddCFKAoh7w+g4lwnZWc7mDVv/DjkaOprlYIbvd02Th1t\nZuUNuUFjxzb9AFCrRWYXpvKLH31Ed4cVtUZk3qJ0MqbEUFsZLIEbEaVj090zueN+ke1vlXJgT13Q\nmLFExxhYtT4PrU7NgqWZ7Hi7HHcIQR7wTzpqK3vGjfFPNmRZpn3bYfqLaoiZl0/qHSuCvuuyJFH8\nT7+ifdsRfHYn0fPzKfz1U0TkpyvO4+ruR2XQo4404DGPfC/VkQbS7lqFJspE7e/eYaC4FtkzcWOW\n0Xit45eJ6RJiWPiHb1/EHYe51gkb7esUU4SOVTdeXMy1o83CO6+dpb15gPjECFatz2XBskwO7K7l\n5OEmentsioSu7W+VkpEdQ87UeDpalT2+9Ub/SnN2YSpN9eZx5TITEkfiS7IsYx10YTRqg5LnWpuU\nLUJ9Xonebju93XY62wdJSY9iSm5cYP9A3wQPSr2a5PRIOtsGcdiCm2wM96a+EAxjVtSCCLHxobPR\nFyzNoLGuL5DhbzBpePBLCzi6r4Huobp5r0fi9LFm7nloHgj+mvfRWC0uPny/mr4eG80NoUuJRqPW\niCxekRmoUzeatCxfk82+D2rGPcZmnbyNPsZS9v0/Uv/idvBJIApkH93E7P94QjGmdcsBvyb40Mxt\noKiGqp+/yYLfPAWApbSBkmd+j+VcHfqUeCJmTMF8vBx8EoJGRURuGsce+iGCIJJ6+woSVs3F0dpD\n9Lw8Gl/6O7ba4G5eDH3dRZ2W5JsWXeF3Icy1RNhoh7lgtr5RQk2Fv4zMZjOzfUspGo3Iji1leEKs\nLJxOL6VFHdx270wqz3Uq1LWmFfiTm9bdPBWTSUtVWTdGkwZBhOKT7VgtTjKzY9l4l19wpqWhn3de\nP0t7ywDRsQbW3pyvWD1HRetoaw593S6nl9KidoXRzp+RyNEDDUE1zODPnv9f/7yCQx/W8farZxX7\n1BqRBUsvvOPSsjXZdLYP4rD7jX/BnBQK5oQuJVm8cgod7YOcGMraT0qOICbWEGifOowsgc3uJibW\niL9PsJK9O6rO6xLPmx5P/vREXC4vHW2DvPHn06y6IZf0rBhuv28Wfd1WSs92Bk2m1BqR6bOSh634\nhAAAIABJREFU8Hp8HNnfQENNL/OXZDCnMO2C35NPCs+Alda3D/gNNoAk0/rOQaZ9+yG0sSN6Ataq\n5qAYgvl4Oa4+C7q4KMp++Ce/kcbvHvdYbMx59kkGK5rw2p20vvlhYGXdd9Qf6xZ1GgSViqSbFlNf\n+67i3DGLZ6AxGpAlidQ7V5B+9/glX2HCjCVstMOcl6qyLopOtFBfozQQA2YnRw82hjTYwySmRKDX\na/jCV5ew74MarFY38Ykm+s0O/t9P95OaEcVt985SxJ833jmTQYuTxOSIgCtz+5ZSGoZczV0dVt5/\ntxyn08O50x047G7sds+E8duxAiBzFqRx850zKDrWis3qxuvz4fP4iE+M4IaN+RzZV09Wdgwp6VEK\nL8HyNdnoLkI9bdGKLNIyo9i/q5bOjkEkn8ShvXWsvDE3yE3rsHsoPt6KfcjAN9aZ2ba5lPTMaIUn\nQRShqrRrXInRCyn5GjA70Rs1fPBeRWB8bUUP33h6DQajho62QYXB1ulUpKRHMX9JBjPnpvCrn+4P\nXFPxyTZyp8bz5LdWXZJAzZXC53DjG+N69lrt/rjzKKOdsGoOdb97F2m0zndzF4dufZpFLz6NrVrZ\nB9zdM0Dza3uwVrfgHXSEVB2SXB5a3thD9uO3EbNoOv0n/cmYprw05v70SaJmZV/GOw1zPaH6wQ9+\n8IOrfRETYbd/elxxoTCZdJ/qeygrbuflF07SWBvswjYaNcxZkEZdVfBqT6USmLMgjY13FiAIAlHR\neuYsSMM26GLfBzW0t1joNztoaeynt8fOvIUj8UONRoUpQhcwarIs8/e3yxQiMC6Xj9rKHvp67FgH\n3Yp9JpOGqBg9jiFDnj8jHku/k+1vlXLmWAtqjUhaZjS5UxNYsS6HgjnJlJ3twNLvYtDi4vTxFsqK\nOyg60crMucnk5MeRmBzBmpvyuHHTxUuTut0+3n+3nK4OKz1dNirLutDpNGTnxSnGlRa3c/xgk2Kb\n1+Pj0ScXc+Z4a+AeZRnMvY6gbl2i6BetGS8eP5rE5AhKz7QreqU77B70RjWCKLB/V61ivFan5p++\nu5a86Ykc2d8QdJ3mPgfxSRGkZX5ySVLn+22pIwz0HinF3jBSMpewcg45j9+mGGeckgKCwMC5eiTn\nyPk8/VZ8Lg+CKOBoHsnqF/VaHE1dfiN/nhmSpayRhc9/m5iF04lfPYc5//kkxozxS+gu5L4+rYTv\n6+LOOR7hlXaYCTl1tBmnPTgpKTJax4q1OdywaRotDf1UlnYiSZCcFknhknRy8uPJnZagWE3WV/ew\nY0sZ3jH9shuqe/H5JOqqeik60YJaLbJsbTap6X4DIAgC8YkmBsa4iUO5tgFsNg+b7p2F5JOJitFz\n9mQrp4/5V0vmXgdbXimmq8Mfr86cEkNjfV8gOxoITE5cTi8nDzfxzR/cSEJSxEW9bz6fxO5tlZw9\n1Ybd7lb0sZYlf0Lb2puVKnLZuXGYIrSKmHFsvJG6yt4gF/lo1BoRrU5FTn48TruH2hCTqNGIokB0\nrIHGuuCYt1otkpwaGaSeFxNnRKf3x+dtg6Frwbs7LqwE8JOk8Nf/TOVPXsVa34YpO4UZzzwSctzU\nf74fZ3svjX/aqdjutViZ/vQjlHznDwyeq0efEo8+LY7+CxRSkZxuqn/5Nxa99Mx1VRoX5soRNtph\nQtLdMci2t8qorugO2jdnQSqPf2MlHp/fmH/pH5ZRVdbNoMXJ/EXpAcnNQYuLk4ebMBg1LFqeSXlJ\nZ5DBBr9CWkVJB6+9dDowQTh7qo0nnlpOaoZf0OTmz8zgj/9zVLEyHA+1RiQnPy5Q373znTLFfrfL\nx94d/ofuESA+afyyJY9H4oOtFTz8+MUlC7324imKTrSOu7+91YJlwEFU9EhSWkyckTU35XFgTy1W\ni5vE5Ag23DadtjFJfGPxeiS8HonSog6mFiQQEaXDOlQ6F6pmO2daPJoQsqh6g5pla3LYsaVUYbBV\naoEbN+UHXN8LlmWyd2e1olRPFGHazMmn1KVLiGHuc1+7oLEpm5bRsvmjkbItUUBGoOnVXaTdsYKE\n575GRG4aLZs/CjLacStnk7i2kIY/bg9q0dn5/nGqn3uDad968LLcU5jrm7B7/ArzaXQJybLMn393\nnKrSLnxjjGx0rJ4HvriQrJy4wH0JgkBCkom0zOiAznhjXR8v/PIwZ0+1UXa2g+qKHvKmxlNeomxg\nIohwwy3TqDjXSWvjSNzW7fJRdKKF+EQjKWlRxCWYKDrZGjBGEzG1IIE1N40oTZWcbh83/uu/X0JO\nJoax291YzE5EwV8zPZrayh62vlHCoY/q6e6wkjc9gf4+O5v/WjThNbpdPgb6ncwdCgvU1/Sy+c9n\nqKno8Qvh3D6dux+eR3JaFDGxeg7vq5+w0ccw/X12ZBniEozc88hcHnhsEfU1vQHFtogoHbffMwsE\nqC5XTshWrc+lYE4K7752Fsdo17sMK9blEpfgv3ejSUtSaiT1NT14vTJGo4ab75hxUQl6l4PL/dsy\nZaegS4rFa3WgT4lHGxdF38ESLOfq6T1Ygs/qIOO+dcTMy8PZ3oujtcefbLZ+AYtffIaE1XOIXTiN\ntu1HlJ25ZHD3Wch+bNNVua/JQvi+Lu6c4xFeaYcJYnDARUtjcDeiOQvTuGHjVJLTgjt5jeXA7lr6\nekeSgBpr+5g1L4W5C9MoLfZ3/TJFarntnpksWZXNX353LOgcDruXXe9VMnt+Gh6vj6hoPe0tI6vO\n6Fg9sXEGGmqVbt7q8m62vFLMPY/MA2DlDTlBBmo0KpXATbdNo6qsG6vFRe8YAz9gdrJvVw2HP6rn\n1ntnsnq9v1Ru0OLi9ZdOYR66z/rqXs6eavXXW19AMlhFSSdbXiliyapstrxSHLi33m6/8tiiFf7k\nvLgEE4VLMjh5eJz0+FFIkn9C0N1pY+ubpRzYXYfOoGH9rdNQq/2Z7/FJJqbNSqKzfdA/iZJkps9O\nZtNdMwGCmlEIooBKrXTtzl2QxtwFaX6FNIFrxvWb9dB6sh5aj2fAyt7lyhV6566TONp7MaTGM+8X\n/0DBv1rwWOzUPb+NA3c8g9qoJ2ndfEVcfBgpRB/uMGEuhbDRDhOEwaghIlJHn2vEeEVG6fjs5+dj\nMI7fBKP4VCtnT7aiUon0dAc3mbBZ3Xz+ySXUVfXQb3Yyd0FqwJU+b3EGpUXt+MY823q6bRw72MCH\nO6sx9zrQaEQMEVrS0qNZtSGXaQVJbHurlP2j6oolCY7ur2fdxnziEkzMLkwjOlbPgDl0XDg7L5aN\nn5nJxs9AdLSRf/zim4EOZ6PxeHycPtocMNqnjzYFDPYwvd3jr+jH4nR4OfxRAyVn2hUxb/A34rBZ\nXRzaW0fxqTYkSWZKbhwyEhqNeqhGu2ecM/ux9DsDsfCOVgtf+/Zq4pP8q2WVSuTBxxYOlaLJis91\n9vwU9o1KRJs6I5HsvPiQrzGZssUvJ7Ikj5SKDW/zSXitDppe24MhPYGE1XMp/pdf0/n3kQln/4ny\nkBO2pBsXXOlLDnOdEDbaYYLQaFWsWJfDru2VuBxetDoVy9dmT2ywT7byxp9OB2KhWp0yZqo3qAO1\nvFk5sUzJE1CNatnpcfnQaNT4fEpjmZQayZF99QHj6PFI6HwyD35pARGROtqaB+hsC475ShKcOtLM\nTXfMoKWxH/sEgiA5U0cMklarIiragMMeOqlqdEw9Mlo/7jlDYTCokWSCWqEODriC+pWr1AInDjay\na3ulwi1+232zuGHjVHxeife3VtBY14fD5iYqRkdDrTkoo3wYS7+TM8f874fimozBcqq33z+bhOQI\nmuvNxMQZWLdx6jWxku784AQ9B4rRpyWQ/aXbUOmC730YbWwkiTcU0vb2gcC26Pn5nHrsJ1irWxE0\nKhLXFdJzUCn5GspgJ960kJn//tjluo0w1zlhox0mJOs2TmXGnGQqS7vIm55ARlZwh6vRnD3Vqkhe\ncrt8gc5hGq2KJSunkJ4VzSvPn6S2qgeNRkXhknRuGXLJHthTi3OMMUtOjWDj7dN5489nFNutFhft\nLQPoDRpe+vWxcTOrI6L9caGu9kE8IdpxDmPuUa6Wk1Ij6WwPbbQlSaahtpeS0+001ZuDjO1EuFw+\nvvyNpezcWkFzgzL8MPYctkEP72+tCIpjNw1lfKvUIrfeM1Ox7+DeOj7cWcWA2Rnyugym8Y3UaARB\nYPnaHJavvbAubp8Gan7zNpU/fTUQa+7eV8zS174/4WRk3n//4/9n77zDoyzWPnxvySabbHrvCQlJ\nIITeQxMEBASxoKKgR7Fz7A09tmNvHD1H8XwHGwoqiqI06VV6Cb2k9942bZPt3x8Lm2x2E0IoAs59\nXVwX77wz884km33emXme34Nbl2Dq0wtxjQqiPq2A6t0Wp0az3kjZ+v1IFe1/hbrFhNJ33pMi/7Xg\ngiGMtqBNgkI8CArpWIYtR9ukXbr6MvnW5oQlK5Yc5eDeZqGKTavTCAn3pEefEIcOZtdeH0/33sEE\nrc8kK705jMk/0I2oWF+W/3i03VCok0dKGDIimoSkQNzcFDQ0OF5tOylsdwUmTO1GXW0TeVlVKF2d\nkMqk1u3r8pJ6Pv9oJ1pt+2eUSle53Ra7yWTm1IkyRo2P5advDllX3M5KucOMXY5eNDw823ZQGTa6\nC/2HhFNaVMei+fuobi3VegHybF+pFP281cY5rGLbYco3H2x321rm7ET8c3dYr3dcP8eujmtMCPUn\nbWPWkVtW4coQX7rcPxknTxW1qXnoq+rwGdRNGHDBeSE+PYILwoDkSNxaZMjy9Xe1W6kVF9quXk0m\ni9e0VCohLNJ2Je8f6EZir2AkEgk33J5EbIIfKndnwqO9GHpNNMt/PEraCfs0li05cbiUojw1S749\n2KbBBovHdUsCgt2Z/dxwnntjLHPeGmv34tKewfbwcmHMpDieff1ahxrjKbsLqKvVced9/RiQHMHg\nEZF2IistkUrhzGIwItqb0RPi2qwL4KJ0IjLGBw+vVlv3EvD0dqx5/lfA0No5zGhCrz63uHLPnrZJ\nXuTurvT812ykylYvUgYjunI1ETPH4RYTTMrD/2LH+GfZdeNL7Jj8Apr89j+3AkF7iJCvi8xfJczB\n19+NLl19cXKSEt3VlxtuS8I/0NbLPDO13C6xR78h4UREedMlzpea6kbMJjMh4Z5MuKk7AUGW9h6e\nLvQfGsGw0V1ISAxk6aLDpJ+ssOp5t0f6qXIKc2vaVQlr1OgZOioamUxqnZdeZ2TX1mwO7y+itLiu\nY0kyJDBkZBSTbkrE2UWOp5eSk0dLbJTk9DojmWkVDEyOYPi1seRkVnFgV9te4S5KOTMfGkhS32Am\n3ZLYrl9BazJTK6whez36BDP2+oSr4mz6DOfyt1WXlk/NoWZnRY8e0SS+fm+bKTgd4ZuchLasGkND\nE3IPV1zDA9BX12PGTFOhrVOgtrSK6v2pSGRSMv/zC+bT3uNNRZXo6zQEXdd2utm/ynfG1YII+RJc\nsUR28bFJytGasdcnUFpcR15WNTK5lMRewQw+nfTD00vJzAcHttu/3EnGnh25NqFklrYuRMX6cOxQ\nsZ1KWnWVps0MYmcwmc2YWshRnolTTz3WvCKSySQYT/fj7ulMo0ZvIy5iaQjHDhZzzXVdcVM506t/\nKAa9keU/HbMx+nqdkZNHS4mJ9+foweJ2lTBVHi507+k4wUh7DBoeRVCoB8cPFePpo+T6qT2oVnfc\ns/1qo8eb96Hw8aDmUAYugd7E/P0mpIrmM36zycSptxZRvvUQUoWcsFtGEXXvRJs+ZC4Ken30d3K/\nXcvxl76kMbcU9YE0XCMC8egXR21Kms0RRN3JXEo3pdiNpamg7fBDgeBsCKMtuGR4+7oy+7kRZKdX\nonRzIiTs7DrVVi/p7Crc3Z3tzp8BQiM9mfngQI6mFPLNf/fZ3JNKJIRHe5HTQrLT3dPZJsQqITHQ\nJglIXla1XVy3q0pBj17BKN2cSB7dhYrSek4eLWX3Hzk2Mq8VZQ3s+SPXuo3tF6DCRSm3W6l7ndmq\nPot2dbekwHbvt0fLlyi5AwW0vxJmk5m4p29DKnf8c8j4ZCmZny61XteeyME1KsjhmXfRsu2YtM2/\nT01eKfF3jUNfVUdjdnFzRZkUnyGJlG88YJNfW5UQcQFmJPirIox2O+jrNRybMx91SjoKH3ei7ptE\n6NThf/awrmikUgkx8X5t3j+zjX3GsW3FkmNs35Rlve/r74qPn6tV4czZRU6fgRYlrqS+ofQdVGzV\nGQfo2j2A2+/py4ZVqVRVaAgKcWfYmBi2rsugqlJDUKg7o8fbnhMbTSbMrYypXCZl6h09rWFqnl5K\nFAo5WxzknT5TJz+3mm/+u4faVjHY8YkBDB4RBVhSdZaX2ibnkEjB20fJ4BHRjBrfFUHnMWq0HHry\nE6p2n0CudCb01lHEPXWbXb3qfadsrk2NOso2HuhwfLVEIsHZ293WaBtNuPh5EfvYzRQu/QOjpgmf\nIYl0e3Hmec1J8NdGGO12OP7SFxQu2QJAQyY05BTj3T/hrFl6BOeOyWRm2Q9HOHmsFIlEQmKfICbf\n0oOMVNsVb2W5hrHXx6GubKQwv5aAYDeiYpu35G/7W18Cgt0pKarF18+NMRPjUDjLmdLCix1g0s2J\nbY4lLNILlbvtalzlrqCsuJ7gsGantJysKoce2QV5aqoqNezfkWdnsGPifbnv8SHWs+Xrp/XA00fJ\nxt/TrLHkZhPU1ejomuBPQ72OdctPUl2pITDYneumdne42yBwzMm3vqX4t+0AaIH0j37GIyEKXW09\nclcXgicNRiKTofCxj5KoPpiO2Wy28wMInpJM9b5U62pb1TWMiBnjKHOwFV57MpfuL99F16duw2ww\nInPpuE+CQOCIThltvV7PnDlzKCoqQiaT8c477xAebqs7nJiYSN++zW+pCxYswGQynbXd5UTN4Syb\na115DUXLtxP7yI1/0oiuXrauTWfHlmzr9bZ1mfj7uznMXe3qpiBlTyGV5Q0UFdSQl63mntmDCA7z\nRCaXcu2kc0+f2ZJVS47bKZTl59Yw74NtTL6lB4NOn8P36B3MhpWpdlvfB/cUoK5qJDjUXu7Vy9vV\nxghIpRJGjo1lx6YsGwEYvd5Ifm41K38+Tmaaxcnp1LEyatRNzHhgwHnN769E3SnbcCyzTs/hZ+ah\nr7QI8vgMSWTgdy/R5ZGplKzdg0HdrORXcyCNnTe8iM+AbsTMnorCx4OiFTso+GkzcnclUn8P/Eb0\nJvaxm6naecwmBSiAxEmGz0CLmI1ULoM2tuYFgnOhUyFfK1euxMPDgx9++IGHHnqIuXPn2tVRqVQs\nXLjQ+k8mk3Wo3eWEc0ArQRG5DPeul+9LxpVMQSuvcoDcnGoGJEfg7NJsuOMSAygvraeyhUxqVYWG\nnS0M/vmSm13lsLxJY2D31hzrtbevKxNv6k5gsL1xzs2sJLqrL54tQq9cVU70HeL48xPaSrzG01uJ\nf6CK7Axbr+SMU+V2imqCtlGG2B/FnDHYAFW7jrNj8otU7TyGR48udnWr95wk89Ol7L3rLTTFFRx/\n6UvU+1PRVdTQVFCBSdOEsb6Rw0/Ps/Egl3u4EvPwVILGt+9cKRCcK51aae/atYupU6cCMHToUF58\n8cWL2u7PIubhqTRkFtGYX4ZEISdkcjIB1/b7s4d1VeLpbS8J6u3tyuAR0QSGeHDicAme3i4MHh7F\nL98dtqvbkZSdHcVN1Xa4RevQjkHDo+g9IIwPXt2IuoWYicJZTlSsL3c/Moi9O3Ixmcz0GxhGTILj\no5Ub70gCCRTmqvH0UjJibAxe3krkTrZ5reVOMqSyqyds60JQczSLvEXrMRsMhN40Et/kHtZ7sU/d\nSn1GIeqD6chcFMg93exSZ9Ydz+bYi5/j3j2qzWeo96WS+s53dm1rjmZRtGw7+irbmG/PXrEk/EOc\nXQsuPJ0y2hUVFfj4WM4RpVIpEokEnU6HQtF8XqPT6Xj66acpLCxk/Pjx3HPPPR1qdznhP6o3IzZ9\nROHSbai6huGXnHT2RoJOMWZiPAW5arLTK5FIIDbBn1HXWZywomN9iY5t1gdP6hPMkQNF1hWnwllG\nj97BF2wsw6+NoaSo1mGCkUgHQijOLnL6DQ5n89p0a3hZr36hePu44u3jSkS091mf6eGp5O6H7Fdl\n3XsGWfNySyTQq3+Iw1zYf1VqT+Sw7663aSqyrHKLV+2m16ePo8kopD6rCM+eMQxd+Q5Vu46j8PWk\nbMN+Tr250L4js5mG7CLa06XV5JYgdXbC1EJZzTnIBycvlV1dhaebXZlAcCE4q9FesmQJS5YssSk7\nfNh2pdPa0xbgueeeY8qUKUgkEmbMmEH//v3t6jhq1xpvb1fkf+ZZkL87Ic/een5d+J89leWVyIWc\nl78/vPLeBE4dK0UqkxDXLaBNIZCR17rjrHBi9x/ZmMwwaFgUyaPstzY7y/BrYunZJ5SdW7Mx6Ixk\nppWjrm4isos3d9zbH2cXew3vux4YRM8+IaSdKCc82otBw6IvSAasx+aMYvPaNIoLa4np6seQkdGd\nFki5Gj+HFb9utRpsAH11HSdfmE9D3ukYe4kEY24Jgz6eDUCXYd2RNWlJ+/J39NX1Nn2ZNO3napca\njMTeNY7M7zZg1GhRRQbS94XpBI3oRcW6fVTsOQmAa6gvvZ66+bx+3lfj7wrEvC4EEnNHLGcr5syZ\nw6RJkxg+fDh6vZ7Ro0fzxx9/tFn//fffJyYmhn379p1TO4Dy8nOTGrwcyF24lsJftmE2GIi+IZng\n+yZfVUpUYPmQXom/m7Mh5nXl4O/vzuYHPiLni5W2N2RSm7SaLqG+XLPjM2Sn5UaLV+0i5aG5mHXn\n5hvgPbAbySveofpgGrXHcwiZkoyTh2VFbWzUkrdoPfraBsJuvQbX8IDzmtfV9rsCMa9z7bMtOuWI\nlpyczJo1awDYvHkzgwbZSvJlZWXx9NNPYzabMRgMpKSk0LVr17O2uxoo3bCfE698RdWu41TvSyXl\n1QXkfvX7nz0sgeCqJPz20TgHNh8/SN3sfSNMOiPmFlveFduP2hlsucfZt7NDbrJoNHj3iSNyxjir\nwQaQKZ2Jvv964p6+7bwMtkBwNjp1pj1x4kR27tzJ9OnTUSgUvPvuuwDMnz+fAQMG0KdPH4KCgrjl\nlluQSqWMHj2anj17kpiY6LDd1UTp+v0YW26zGU1UbD9K1KxJf96gBIKrFM+kLvT7eg75363HZDAS\nMnUY2Z8to+KPI9Y6/iN6IndrTpaiDPG16ydgbD80+WWo956yu3cGQ01Dm/cEgktFp4z2mRjr1jzw\nwAPW/z/77LMdbnc5oz6UTu63azHpDARfP6RdoX8AhQOnFEeOKgKB4MLg0y8en37NsfnefeJIn/sj\nmvwyVHHhxD1zu0396FnXU7HtCBXbj4DJjEdSF+KfuwPXyECq9p2i7lQ+qe9/h77cNgyxbGMKXZ+Y\ndknmJBC0hVBEa4e6tHz23/Oe1dGldPUeen40m5Apw9ps0+XBKZRvPkjNYYs0pUdcGNEPTrkk4xUI\nBKDwdifxzfvavC9zdabP/54m95u1KIN9CL15JFIny1eh78BueHSLJPVtew9zoWYmuBwQRrsd8hdv\nsvFMNdQ3UvTb9naNtsLHg6HL3ib/x02YmnT0eWwqNbpz9vUTCASt0Faoyf7fCvQ1DQSM64//9Gs6\n1U/W/BVkzvsVbUkVrlFByNyUhEwear1fvHwH+upWjkUSCWG3jT6f4QsEFwRhtNtB4iBkRyI9u++e\nTOlM1N8mAKDwVMFV6DEpEFxKjBote25/ndqjFmnhgiWbUWgakcVHcurNhTRkFOAaGUT889Px6hPX\nZj86dT1Z836ziqRockrI+HiJRYP89N+2Kj7cLh5bFR+G1NmJvTPfwqTTE3TdQKLumejwGQLBxUQY\n7XYInzGO4mXb0ZyO+ZR7qgi9ZdSfOyiB4C9I/g8brAYbLEY8/du11GaXWvNTN2QVo6usZdia95HI\nbLUd1IcyKF65E31NA00llTb3GvPLMdRpcPK0+J749E8g5MYRFC7dillnQOHvRdD1Qzjy9DyrM1rl\nzmNIFE5E3jn2Yk5bILBDGO12UEUFMWDRy+R+swaTVk/wDcn4j+gFgFGrp+CnzRjqNITfca1DBzQA\nrbqOo8//H/WZhbhGBBH3/HSUgfaqWgKBoG2MLVa9Z6g5kYe2hY44QM2RTGpP5eGZGG0tK1y2naPP\n/rfZ+1shhxYhX6r4MLuQr14f/52wW0ZQezyH4BuSyfz0VxvvcbPOQPmGA8JoCy45wmifBff4cHq8\nfb9Nmb5Ow57b/4l6fyoAud+uoe/8Z/HqGWPX/o+73yN/xS4AKjmKJqeEwb+8ftWJrQgEF5PwO64l\nb9E6GjKLLAVSCYZGewUzJx93lKEWfffaU7kUrdhF4S+bbcO1zhhsmQRVfCTdX7vX7u9RIpHgN7wX\nfsMtL+lyV/v4b5lKaVcmEFxshNHuBNlfrLQabABNdgnZ81fQ59MnbOrpKmsp/eOoTVnVnhM0ZBai\nig27JGMVCC4WhoZGTr7+DXUn83AJ9aPrk9Nwj7s4WfBqjmZh0uqRyGVIXRREPTCZ3Pkr7Or5juiJ\nRCYlb/Emjr/8BcZaTdudGs0ovNzw7mc5Azfp9Bz7xxeUbzuMSavHd0h3ev/ncaROcpxDfJG5umDU\nWPTonUN8rX4rAsGlRBjtTqBrtSUHoFfX25VJXZyQu7mga3FP5urSIfUlgeBy5/BT8yj+bbv1uj6j\ngGGrP7Dkjr6AGJt07L/nHYx1lixqxvpG8r9bj8LTDUN9o03d6p3H2TTgAUw6A8YG+4QvrWnILMJs\nNCKRyUh97wfyvl1rvVe09A9qj+fQ7ZW7SX17kdVgI4GIO8Zajb1AcCnplIzpX52g6wYib7k1JpHg\nO7SHXT25m5Lo26+xaCGfrhc8eSguAWfP+iQQXM6Y9AaqTifIOEPtkSwqttmnTT1fipY/pM4AAAAg\nAElEQVRttxrsM+hKq/FKirarqy1To6+u75DBBnCNCrI6rdUcybC7X5+aT/rcnzC0XLGbofZYll1d\ngeBSIFbancBvWE+6v3kfhT9txqjV4X9NH7o8fIPDugPefxDnblGoU9LxSIwk9KaRl3i0AsGFRyKT\n4qRS0vJUWaZ0xiXwwr+Q1mcUOhgAxN41jrK9pzBUnWNI5en0mx49ool//g5rsXMbDqJmk32udrm7\n67k9UyC4QAij3Ukipo8hYvqYs9aTSCSETE4mZHLyJRiVQHBpkEilhN02mrQPF2Nq0gEQNHEwHon2\nq9/zQV+nofCXrXblqu5R+A9IcKil4BCpFJdgH7z7xxNx51hcgn1RxYTYhIbF/P0mKrYeQlumbm6m\nVBD90A1kzfvNGnKmDA8gepaI0Rb8OQijLRAIOkXsozfh1bcr5VsP4x4XTujpLFgXEnVKGk2FFTZl\nUoUTgxe/SsY3a9FX2PuXOCLizmvp+eEj7dbxSIjgmn3zOfjgh9Sl5ePs60nozSMJu3EEQeMGkrdo\nHcZGLeF3jMElQIRtCv4chNEWCASdxi85Cb/kpIvWv3tiFHJvFYbqlo6eZlIemotvt4gO9eEc7Evs\n47d0qK7cRcGAb160L3dzoYvIISC4DBBG+wLTkFtKxr+X0FRchUdiFD4fPHD2RgLBFUJ9Tgl5C1Zj\n0uoJvXkE3v0TLt6zMgs5cN8HrQw2mHQGqnYco/54Tof68eoTK3JcC64ahNG+gJiNRlIe/JCag+kA\nlG9KQVqvIf7dh/7kkQkE548mv4y9t/8TTXYxYFEa6zPvSQKu6XPefZv0BvK+30BjYQVB4wfi3S+O\nzE9/pe5ETpttdA7CLJFIwGyboKepqNK+nkBwhSKM9gWkfMshq8E+Q9GmFOJMpg4lGhEILmdyF6y2\nGmwAfWUt+T9sxG94T4yaJpw6oT9g1Oo5+fo3FkngWotqWe5Xq+j+z3vRVtScpbUDzPYZ9Uxanc21\ntlxN/k+bcfbzJPSmEda0nALBlYD4tF5AnDzdkCicMOuadZLlShfL238HMJtMFP++m4aMQoInJ6OK\nCblYQ6WppBL14Uz8hvVE7mYv0SgQtMZsMNmV1acXsG3U42gr1Hj2jCHpg4dxiwzqcJ8nXvuK3K9W\n25QZ6hrJ+34DgeMGULZuX/MNmQwnlQv6mgakSgUuvh5oCio4G77DelJ7KpeTry2g5kgmhnoNJq1F\nyrTgx00M/P4VdNV1ZP13GbqqWvyG9yRcpOEUXKYIo30B8e6fQMDoPpSu2QuAxElG9O3XdEhn3Gw2\nk/LgXIpX7gSTmaz/LSfxjVmEdTCrmNlkoujX7dRnFhI4tj9efbq2WTf9o5/I/nwluspaXKMC6f7a\nPQRNGNyh5wj+uoTeeg2FS7ehLasGQOLshCa3xCpkUrH1MCdfW0D/r+d0qL+q3SfI/36jw3vGukZi\nH70JfXUd5VsOIlUoCLlpBKE3DqN41W68+8UR1DWY7X//lJK1e8DBC4Xcw5WoWZPo+vRt7Jn2KlW7\njtvVqdxxjJyvf6dw6TZqj1hCuop++wNtmZrYR2/q0DwEgkuJ7LXXXnvtzx5Ee2g0urNXuowInjQY\nha8nqthQujxyA70fv8lmDtpyNcdemE/6f36mctsR3OMjUPh6ULHtMKnvfQ9Gy5ePqVFHfUYhmrxS\n6jMK8UiMblceMuXBuaT/60eqdh2naNl25O5uDg13Y1EFhx7/D/rTghR6dQMNuaVE3Dn2nJKYuLk5\nX3G/m44g5tU2LgFeePWNw2w0ouoaikdiNDWHbFXEzCYz0bMmnbWvuvQC9s180zaRRwvk7kqKl+/A\nycONvv97huj7JuHTPx65Sol3n664BPngHeKL19gB6CprULcah5OXCr9hSQSOG4iznyen3l5kkx+7\nJcZGLTUpLY61TGb0dRoiZ4w76zwuBuIzeGVxMebl5ubc5j2x0r7ASBVORN9/fZv3Dz/5CWXrDwBQ\nk5KOJr+M5FXv0pBdjFlvq7xUn5ZPfVo+AGUb9hNyyyjcY8Pw6h1rU69y70lKVu+xXhvqGsn7bh1R\n99gnNFAfzLAa7DM0FZRj1huQKJysZbqqWkx6Ay4ijaigBb6Du+M7uDsA6iMZlKzc1azJDSgjOual\nXfDjJrSl1Q7vOfm4o8kpQZNTQvW+U+jV9fT74rk2+0p86368+nal5nAWUqWCyp3HUe8/Rcmq3ZSu\n20eXh6biEuxDfa3jFwSzyf4cHByVCQR/PsI76hKir6mnukV2MLCIRxx64lNCbhqBc5hfm23LNx/i\n8OyP2XnDixx67N+YzWZ06nqayqppzC+zOUe3PMtxdiP/kb1Qtgp/ce8WifS0wTabTBx+ah6bk2ez\nefDD7J35Fvr6djIlCf6yePWMJWrWJJx83AFLTHXcM7d1qK20xQtiS9y6htq9VFbtO4VJ53iVDBbV\nwbBbriHxjVlE3TOB+tR8q801640ULd1G1L2TUPh7OWzvP6o3HkldmgvkMoInDunQPASCS41YaV9C\npC7OyD1U6FvFnRb+tAmvXjG4RQShPYtjjalJR8Ev29BW1KA+nIGpSY9nUhdLUhJj87meKt5xikS5\nSkm3V+8m85OlNBZW4N4tku5v3Gu9n7doPfnfrbdel63bR9r7P5D4+qzOTPmqpWLnMWqPZRE8eSjK\n4LZfti43THoDZZtScA7wxrsdv4eO0u2lmUTfP4mGnFK8+8XZHeGYTSZqj+fgHOhtkygn8m/jKV6+\nnfr0Zl1xuacbnj1jaMwvt0qjAjh5uCLpYOawurQCDHW2K2pDfSMhU4cRNHEQmZ8spfCXLegqLS8G\nHj2iib7/eiLuuLbZEW1YT8I7IFEsEPwZCKN9CZE5OxExfQyp7/8AphaOM2YoXrXT7lyuTQxGyjem\nWC8dOdi0l0ksZHIywdcPxaTVI3NR2NyrdRAX25BVbFf2V+bw0/Mo+GkLZp2ejP8sJfGNWYTeeH4S\nnob6RnIWrMZQqyH0llG4x134fOt1afkcfOQjao9mIXV2IvC6gfT971M2+tttYTYayV24jrqTubh3\njyJy5jhrGKNLoI/DY5T6jEIOPf4f1ClpOHmoCL1lJIlvzkIikeAS4MOgJa+T8+XvlKy2REwYahoo\n+mUbyshAGnNLAZDIZfiM6IVEKsVsNqPJKUHh54mTg4Qd2soaTrz0hd3Ots+gbii8VAAkvjGLiBlj\nKViyFbm7kqh7J1r68lSR+IZ4MRVc/gijfYnp+uQ06tLzKfplm025en+avaOMXIZXzy7oqurQ5JQ0\nlyvkoDO0+5z2thPBsqXY2mCDZau8NW7RwXZl2Uu2cOLLNZiNRoInDSHizrHtPu9qQX0oncJftlqP\nI3TlarLnrzgvo62v07DrppepPZIJQN73G+jz6RP4j+p9QcZ8hhOvf2NNemHS6iletoP80X2JuP3s\nq8ojz3xm4+ldcziTXv+abb3WVdWS9flK9Op6gsYPxH9Ub1Lf/x716eMgvbqO3G9WEzCmLwGj+wKg\nDPYl5uEbbHJYA+ir6/DqF4f6QBpmg5GixRuRuyio2n0C9cF0nP29iLxrHP7v3m/TLvebtdSnFdiU\nOQf50vOjv9uUucdH0O2lmWeds0BwOSKM9p9Aj7fvpzG/nOq9lnzETj7udud4AKE3DqfPp0+gq21g\n38y3Ue8/hdlgRKaQY2xttOUyMFgc2aRKBYHjB3RqbJEzxlJzKIOS33dj0urxHZZE3HPTbeqUbz1E\nyv1z0Z/OMVy5/RhSFwVhN1+6tKP1WUUU/rIVhY87EXeOc/gCcjGoPZGLqbGVWEeFuo3aHSN3wWqr\nwQbLi0Dut2svmNHWqetYO+NNyjfst7tXvuUQ5ZsOYmzUEjiuP5Ezx9vV0VbUULJqj01Zyeo9dHv5\nLhTe7hg1Wvbc/k9qDlvmUPDjJhLfvh/N6dXyGcx6I+qDGVajDWA2mjCZbMO1jDo9tcezrdeG+iZy\nF6zBWG/Jqa0tqSJz3m/E3zoSwptfKFv7dYAlWmPPra8S++Q0kWlPcFUgjPafgMLLnaG/vUnRqt2W\nre6thyhYvMmmju/IXvT+5HEAnNxdMWt1mE8bZWN9E1JXZ6RyOSadHt+hPQicMJDSNfvAbCZkSudT\ngUpkMnp99He6vXQXRp0eZbCvXZ2SVbusBhssilNl6/ZdMqNduvEAhx//BF25xVgW/baDwUv+eUkM\nd8iUZNI/XmLdvgXw6hXbTouz0/JneQZDQ2O7bUw6PUXLdyBzdSHouoHtKu6lf/gjRevtDTZA6emX\nM4DyLQcxm8xE3X2d7bP0BrudG5NOj0lveXHM+2G91WADGBuaKFyyBVXXMJuQMKmLAt/kRJt+nAO8\n8B/Zi5KVu6xlHonR1BxIs6lnbNLaXjc0Ubz5EEF3NRvt8DuuJX/JFpoKyltUNFJ7PIdTby0i8Nr+\nyJRth9IIBFcCwmj/SUhkMkKnWAyrS4gv5RtT0J42Qs6B3iQ8f4c1btpQ30hDtu25skmjpdfnj+HT\nLw5lqD8AUXfZftm2RcFPmylZsxd9dR2q7pHEPXkrzn6eNnUUvh5ttpe52iuoyVwv3Zdh7terrQYb\noHrvSfK+W9+h+OBzpfZkDukf/UxjYTnuCRF0e/VvJL33EFmf/UpTSRUePbrQ481ZNBaWk/ru9zRk\nF+MWE0LCP2a261fQktCbR5D//QZ0Z2Q7pRICRrWt592QW8qBWe9ZtrolEnyHJjJg4UttKts1tFrx\ntqTlkYxZZyDjk6WY9QYiZo5H5mzx8FYG++I7LImyFobfb1gSBo2WE298Q/X+NPt+dQYSXr4LXWUt\n1ftTcfJyI3z6tfgObjbauuo66tML6PXRo6hiQ2nILMK1Swixs29k59QXqTuRa63r7OeFtqTKei1X\nKQm9th8tgyRdIwLp9/mzZH++guLlO60vuQCa7GLUB9PxHdqjzZ+FQHAlIIz2ZYDv4ET6fT2Hgp82\ngURK+G3X4N0v3npfrlLiFhuCusWXozLMn8DRfZGrlO32bTYaKduYgtzTDZ+B3cj/YSNHn/+fdSux\ncucxipZvp+8nT3Z4Ozbq3olUbE6h9lS+dSyR90w812l3GkOd/SpUV9WxvMrngslg5NCj/7GeA6v3\np2JQ19Pvy+dtkmSYzWYO3PQylTuPAVC97xTqlDSGr/9Xh1b/HgmR9PrkcfK+XYtR04T/qD5EP9R2\nGsjMT36xjgmzmcodx8j633LinrrVYX33uHBbOdB2aMov4/g/vqBs00EGfvcSEokEY5MO3+Qk9Op6\nZK7OuHeLJPTG4eyZ9gqNeWWWhi2jF2RSAsf1Rxnow6AfXkFXVYvM1cXmZ5Hx6VKy569AW1qNKi6c\n7q/fS8ILM6z3e/3nMTI+/pnGogo8ukURcdc4Tr76tfVMO+Ku8fj0iqG83PZYybtvHF7znqQ+vYDa\no81b7C4hvnj0iO7Qz0AguJzplNHW6/XMmTOHoqIiZDIZ77zzDuHhzSFGx44d47333rNeZ2RkMG/e\nPHbs2MGKFSsIDAwEYMqUKUybNu08p3B14DMgAZ8BjtMcSiQSEl6YyYl/fk3dqTxcIwKJfezmsxps\nTX4ZB+7/gJqD6UgUcvxH9cFsNNrHdJfVcOL1BYwc9XGHxuoaEcjELR9xYO4vYDAQfufYS5r60Hd4\nElW7mz3mFf5ehN1i2Zo3NmppyCnGrUuodaXYWSq2HW42jqep2p+KSae3iTPW5JZQte+UTb36tAK2\nX/csg356rUMr7sDRfQlscdbbHo4SabRchbYm7pnbMJZWUrjxIIY6DWZ9sz+ExNkJswOlsPItBynf\nlILP4O7snvYq6tPb1cowf+KeuZ38nzY3G2wAownXLsG4x0fgP7wnkfc2v8QpfGx3bRqLK8ia95v1\nRas+LZ/0D3+0eRHySoqh/5fP27Qb8ttbNBVV4OTl3q5evkQqJe6p2zj5xrc0ZBXhEuJHzN9v7FRC\nE4HgcqNTRnvlypV4eHgwd+5ctm/fzty5c/n44+Yv/B49erBw4UIAamtreeSRR+jduzc7duzgrrvu\nYsaMGW11LWgDv2FJDF/7IY2FFbgEercpTtGSjI+XWLOOmXUGytbtQ5Vg7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PYAABbdSURB\nVIl79oyx/E5PI3VR4H+NbVKT6AenULJ2L9rToj5SpYKg6+1FXwQCQec5a5z2n82VHtd3uccmavJK\n2XXjSzSeTmcoc3Um6YOHCbtlVLvt6remkPnrduQebkTfP9kaZpbz1e8ce2G+TV3XmBAiZ4xD6uaC\nzFlO2M0jkTp1TBdcX6ch7cPFaLJLcIsNIf6Z6e2eherrG9kybLbVcIDFWPX/eg6la/dRtHw7AMGT\nk4mcaXnRcBjnLcFOrMXJR4XfsF7EPHYzXkldAMhduJZTby2yONudbhdyyyj6tiPnqaus4dT7P1D4\n81arN7fc3ZWkDx4m9Mb2w+AMDY1sGfYoTUWtVvRSCU7e7kgVcpu5AyCTEjtzLLGv/A25W7OD3cl3\nFpH58c8t5iyh578eIeKOsXbPNen0HPvHF1RsP4rc1ZnQadfYxaBnfvYb2V/9jq5cjcLHna7P3k6k\ng74uFJf731ZnEfO6srjs4rQFVzeuEYH0/+ZFchesxtioJWjCIII7sDqKvmUkqpH2CS4aHSSucAn0\nwbNXDKfeWkh9RiG5X60m7tnp7cYHn+HgQ/+ibMPplJBrQZNTQv+v5rRZP6eFChiA3MMNz96xZHyy\nlJwFq63JMap2n0Tu7kro1GFE3zeJzM9+s/F8ljrJMelsQ5dcI4NxT4jAMzHKWhY0YTAn3/y2uZIZ\nipZswdSko/8Xzzkco8LXE2c/L5vwK0OdhsKl29o02sZGLQVLtyJ3VRJ+x2iy5i2zCRvDZEZ/Wvtc\n7q3CUN08F/9RvRn+1XN2Xyz1p/JsH2I2oz6U4dBoSxVO9PzgYYdjA4t3fdr7P1jH1FRUifpA2kU1\n2gLBXxFhtAV49oim54ePXJC+gicMIvfr3zHUNmube/XtysnXv6HmUAYANYczOfnGN/iP6u1wy/oM\njYXlVGw/YlNWsnovmwY9hG9yD6IfmEzhz1sBi5eya2QQ5VsO2ayQDbUN5Hy+0q5vk1ZH2Yb9hE4d\nhtxNyZDf3uT4i1+gL61CldSFyh3HbHJ2A9QcTKfmYDrGRi3dXroLAL26zmau1nGu2Enqu98TP+cO\nx5NzcLbs6MwdoD6zkAOz3qfupMVBzXtQd/ov/AcnXvqCutaGF8tLkmpoDzR5ZbjHhZPw6t0O+3WU\nfMMtqnNhWOVbDtu+RIB1vAKB4MIhjLbgguLVpyvd35hF/vcbMNQ34ju0B1F/m0B2K8NZn5pPfXo+\nHt3bznEsdXJCqpDbOnuZTGhyStDklFC0bId1tVq0bDsDF72MzIFjXVu0jMX27BbF0F/fxN/fnV2v\nfkPZ+v1ttsv56nfqTuYSMWMsgdcNwjUqGE1WkV29jHlLCZwwEK9esXb3wu8cS8GSLTSePjuXuigc\nJv0Ay7ZzSwNYvecE1XtPEjhhkEOjra+ua3c34gxxz9xGXVo+VbuPI5HJCLy2H9H3TTprO0e4d4uw\ni8XvaEYugUDQcYTRFlxwIm4fY5UANen0HHnu/2yTSwDK8ABczyKu4RzgReD4gRQu2eLwfsvt5ca8\nMnIXrCb8jjGoU9JspEEdIXV2QuZiL71bsT+V1A8WW2OyHT63oYmyDQeoTkljcFgAA759ga0jHrdb\nPZt1Bso2pTg02q5h/vRf8AI5X/+OUaMlcPwAq6xr1Z4T5C5ah0mrJ2jSEIehc9oyNd3/eQ/Fq3bR\nkFZgc0/ewTSUCh8PBi/5JzVHs5ApnXHvGtahdo4InjSEsNtGU7x8B8aGJjyTutD1qY5r2QsEgo4h\njLbgvDDpDaR//DO1x7NRhvjS9clbbaQp0+b+SMEPG23aOHmriH3s5g6F/vT6+FG8+nSleOUOqnae\naLeuUasjZHIyrhGBFC3bQdmGA9SnNq9ElaH+NBZXgMmMSasn6/OVqOLCibijWd875dUF7Rrsluir\n6ihatp1uL91F0gcPcfT5/wNDC8Mtl+HZo0ub7T17RNNr7mybsrpTeRx44EPruXzpun149rSVY5U4\nO+F/TW9kLgoGLvwHOyY8h66FB7v/CMe5tR0hkUjwatV/Z5BIJPT++FFiHrmRxqIK/IYldTolqkAg\naBsRpy04L44+93+kf7iY0tV7yPnydw7Met8mFrn2WLZdm+DJQ4m8azwA6oPppH24mKLlOzA7OOeV\nymVEz5rE4CVvEHjdQDhtCFxC/JC4NHugyz1cCZ5s0Qr36hVL91fuZujKdwi7bTRe/eMJmTocj55d\nbJXZDEYq/mg+MzfpDVQfsRdbaQ+n06vayBnjSF71Pm4xFjlXuUpJxPQxBFx7bik383/cZONIZ2rU\n2YSHSZXOxD873So/6hYVTI/3H8JnaCJO3irknm5Up6ST9936c3ruhcI9LoyAUb2FwRYILhJipS3o\nNCa9wSZ2FyxexJU7j+GXbBFlcQn2tWvnFhMKQPbnK0l99zsM9Y0glRB680j6tAqVasgqomxTCn4j\ne9N/wQtUbD1EY3EVoVOHUbpuH0W/bQcJhN4yioAWccONRRWW5CeH0lH4eOI1JZmmYvsMW0atjvR/\n/4zf8J6oYkLQa9qOc26NZ+9YAidavMeNjVqCpwxj5LZPqNxxFGWYP6rT8zwXJI6MXUvN9kYtTq1E\na0ImJ6M+kEbVzuOAxWHuREYhHt2j8OrT9ZzHIBAILl+E0RZ0GolUgsTJ1shI5HJkyuZt765P3ELt\n8RzUKWlIFHL8R/Um6p6JmM1mchetsxhsAJOZ4pU76fLgFDxPx0BnfvYb6R8vwVDTgNzTjZjZN9H1\n8ea0lyE3DCPkhmE44sRrCyjffBAAfXU9qR8uthj97UepPWpZTTv5elK2/gClq3aToXQm4u7rcPHx\nQN8iXMq9WyQKfy+qdh9H6qzAf0QvVPFhKDxVBE4czL4736D+9Jlywc9b6f3vxwi6bmCnf6aRd19H\n8YqdzdnIZFI7fwCTA5U29WnP/DMY6jSUrNkjjLZAcJUhjLag00hkMoInDSHzv79ZV4P+I3vj3bfZ\nUCjDAkhe+Q5lmw4i93TDZ0ACEokEs8lkkdVsgalRhyavDM+kLhg1WnK++t2aI9tQ00DOgt+JnjXR\nLgOXI1onATHWaqg5mEHyinfI/3EzhnoNmZ/9ZpUpNTZqLRmwzmzRK2S4hQeR8NIMAq8dQH1mITI3\nF5RBzTsHqR/+aDXYAAZ1PfmLN56z0TYbjWjyy3EJ8sE1zJ+Bi18lb8FqjFo9Jr3Bohd/2nCr4sII\nb5VjG3CY4lIZFnBO4xAIBJc/wmgLzouEl+/CrWsY6pRUXMMDiX7APj+6RCazE1KRSKV490+guHC7\ntcy9eySBYy1nwLrqWhqLbLeztUWVaMvVHTLaysgA6k41h0lJnZ3wGRCPTOlM1N+uoy69gFOtVdBa\nnqnrjDRkFnLkyc9Ieu9BgiYOtnuGWWfvsOZoFdwelbuOc/zlL6k9mYtreAAxs6cSOXM83V+7x/IM\nsxmvpBgqth/GycudLg9McTj/Lg/fQO3xbEtGNpmUgNF9Cb999DmNRSAQXP4Ioy04LyQSCRHTxxAx\nfUyH2xT/vpuCJVswafX4JPfA1KTDJdiP2CdutuajlnuqkMgkmFvojUiVzigjOrZ6jHvmdpqKKqk9\nmoWTtztht422SZ6iig3Fq28c6hZOXo7QllWT/cVKh0Y77PYxFPy0xXpWLnF2ImjC2fNTn8FsNnPq\n7UXW7XpNdjHpH/5I8PVDUXhbZAwlEgmRd48n8u7x7fbl3TeO4evnUvjLNlyCfAgY279T2bwEAsHl\njTDagktKxR9HOPzEJ9Ztb6lSQa9/P0Zoq7Pp2iOZmFvJiEpkUgx1jSi8zp7lyqtnDMPXfkDV/jRc\nIwNstrXhdIjSv/7Oqfe+Q5NTgklvtAkPa4mu0rGusKpLCH3nP0PuN2swNmoJHDfwnFa3Jq2ehlai\nLE0lVVTtPnFOxv8Mcjel1StfIBBcnQijLbikFK/YaTXYYDnHLl2zx85oe/aKQRnmb01kAqCKCbXz\nnG4PiUyG76Bubd5XxYXR/8vnATCbTKTN/ZGqbYeoPmybL9yrX9vOXD4Du+EzsO1ntIfU2Qm36GAb\n8RSXIB98Bndvp5VAIPgrI+K0BZcUqYt9di+ps70ymdxNSdenb8M1OhgkEty7RRL/wh1IpI4/smaz\nmeyvVpHy8FyOv/IV2la64WdDIpUS/+x0puz+jH5fPo/fyN549IgmYsY4erx5/zn11eFnSiTEv3An\nHkldQCbFNSqIrk/fZt0aFwgEgtaIlbbgkhJ5z0RK1+23hjS5hPq1uaUbcce1hN44nMbiSlwjAtsV\n7Dj11iIyP11qzeFdfSCV5OVv2+SP7iiBY/t3KAPZhcAvOYnhaz9Ak1+GS6DPOWmnCwSCvx7CaAsu\nKaroYIb8+ia536zBbDARfscYVF1C2qwvUzq3e/8Mpev2Wg02gHp/KsW/7yGkjSQclxMSmazT2bUE\nAsFfC2G0BZccZbAvCXPuvMC92ntKS6TCe1ogEFxdiDNtwVVB0MRBltSQp/Ee2O28lMkEAoHgckSs\ntAVXBfHP34Ey1I+qvSdxCfAh5u83duo8WyAQCC5nhNEWXBVIJBIiZ44ncqaIUxYIBFcvYntcIBAI\nBIIrBGG0BQKBQCC4QhBGWyAQCASCK4ROG+29e/cyZMgQNm/e7PD+8uXLufnmm5k2bRpLliwBQK/X\n8/TTTzN9+nRmzJhBfn6+w7YCgUAgEAjs6ZTRzsvL4+uvv6Zv374O72s0GubNm8eCBQtYuHAh33zz\nDWq1mpUrV+Lh4cEPP/zAQw89xNy5c89r8AKBQCAQ/JXolNH29/fn008/xd3dsUby4cOHSUpKwt3d\nHRcXF/r27UtKSgq7du1i7NixAAwdOpSUlJTOj1wgEAgEgr8YnTLaSqUSWTsxsBUVFfj4+FivfXx8\nKC8vtymXSqVIJBJ0Ol1nhiAQCAQCwV+Os8ZpL1myxHomfYZHH32U4cOHd/gh5haa0B0pb4m3tyvy\ndhJFXAn4+1+dWZvEvK4srsZ5XY1zAjGvK41LOa+zGu1p06Yxbdq0c+o0ICCAiooK63VZWRm9e/cm\nICCA8vJyEhIS0Ov1mM1mFAr7tIwtqa7WnNOzLzf8/d0pL6/7s4dxwRHzurK4Gud1Nc4JxLyuNC7G\nvNp7CbgoIV+9evXi6NGj1NbW0tDQQEpKCv379yc5OZk1a9YAsHnzZgYNGnQxHi8QCAQCwVVJp2RM\nt2zZwpdffklWVhbHjx9n4cKFfPXVV8yfP58BAwbQp08fnn76aWbNmoVEImH27Nm4u7szceJEdu7c\nyfTp01EoFLz77rsXej4CgUAgEFy1SMwdOVgWCAQCgUDwpyMU0QQCgUAguEIQRlsgEAgEgisEYbQF\nAoFAILhCEEZbIBAIBIIrBGG0BQKBQCC4QhBGWyAQCASCK4ROxWkLbNHr9cyZM4eioiJkMhnvvPMO\n4eHh1vvHjh3jvffes15nZGQwb948duzYwYoVKwgMDARgypQp56w+dzE527zg/9u715Cm/jAO4N+t\n2YUML9imIHYRoxokQYVD23RlYTd8IeFoNGNU2jSLrQulzBcWUkuodyFm0ctohV2oCBTKtpokUglq\nhLCsVlmZK6I5f/8Xo0OrzZ3tb54zeD6vPDv7wfOcH89+229nPoBSqQzq9nbx4kVMTExEHCckPnnd\nvn0bFy5cgFQqhUqlwsGDB2G323H27FlkZWUBCDS9qaqqEiKFICdPnkRvby8kEgmOHTuGFStWcOce\nPXqE5uZmzJgxA2q1GiaTKeIYsZgsRqfTiebmZkilUixatAgnTpyAy+VCbW0tcnJyAABLlixBfX29\nUOGHNVleWq0W6enpXG8Hm80GhUIh+vkKF5/H44HFYuGe53a7YTab4fP5RFlLoQwMDGDfvn2oqKiA\nXq8POidIfTHyv9ntdtbQ0MAYY+zBgwestrY27HNHR0fZjh07mN/vZ+fOnWOXL1+erjCjxievNWvW\nxDROSJHi+/79OysqKmJjY2NsYmKClZWVscHBQXb16lXW1NQkRMhhPX78mO3Zs4cxxtjLly/Z9u3b\ng86XlJSwN2/eML/fz3Q6HRscHIw4RgwixVhcXMzevn3LGGOspqaGdXZ2MqfTyWpqaqY91mhEyquo\nqIh5vd6oxgiNb3w+n4+Vl5czr9cryloK5du3b0yv17O6urqQr9VC1Bdtj0+BaFqOtra2wmAwQCoV\n/6WPtZWq2FuwRopvzpw5aG9vR2JiIiQSCZKTk/HlyxchQo3I4XBg/fr1AIDs7GyMjo7C6/UCCHyq\nSUpKQkZGBqRSKTQaDRwOx6RjxCJSjHa7Henp6QACXQQ/f/4sSJzRiuXai32++MZ37do1bNy4EXPn\nzp3uEGM2c+ZMtLS0QC6X/3VOqPoS/8oRB/i2HP3x4wcePnyIdevWcY/duXMHu3btwt69e+F2u6ct\nZj745PXz50+YzWaUl5ejra2N9zgh8YkvMTERANDf34/h4WHk5uYCAJ48eQKj0QiDwYC+vr7pDTyE\njx8/IiUlhTv+1QYXAD58+BC2RW64MWIRKcZf8/P+/Xt0dXVBo9EACHz1VFlZCZ1Oh66urukNmgc+\n195qtUKn08Fms4ExJvr54hvflStXUFZWxh2LrZZCkclkmD17dshzQtUXfacdpVCtSnt7e4OOWZj/\nDHv//n0UFhZyn7I1Gg3y8vKwevVq3Lp1C42NjTh//vy/CTyCWPM6fPgwtm3bBolEAr1ej1WrVv31\nnHDXYzr8n/kaGhqCxWLBmTNnkJCQgNzcXKSmpqKwsBA9PT04cuQIbty48c9ij0Us11rI+eErVIwj\nIyOorKyE1WpFSkoKFi5ciOrqapSUlMDtdmPnzp24d+9exE6CQvozr/3792Pt2rVISkqCyWTC3bt3\nI44Rm1Dx9fT0YPHixdybrXiopaky1fNFi3aUQrUqPXr0KK+Wox0dHdDpdNzxnzeg2Gy2fxd4BLHm\n9Xs+eXl5GBgYiKkF678Sa17v3r2DyWTCqVOnsGzZMgCBra7s7GwAwMqVK/Hp0yf4/X7upiEhhGqD\nO3/+/JDnPB4P5HI5EhISwo4Ri8nyAgCv14vdu3fjwIEDKCgoAAAoFAps2rQJAJCVlYW0tDR4PB5R\n3QQZKa/S0lLub7VazdWTmOeLT3ydnZ1QqVTcsRhrKVpC1Rdtj08Bvi1Hnz9/jqVLl3LHjY2N6O7u\nBhDYKvp116tYRMrr1atXMJvNYIxhfHwcT58+RU5OjuhbsPKJ7/jx42hoaIBSqeQea2lpwc2bNwEE\n7ihNTU0V/EUmPz+f+zT24sULyOVy7tNMZmYmvF4vXr9+jfHxcXR0dCA/P3/SMWIRKcampiYYDAao\n1Wrusfb2drS2tgIIbF2OjIxwv8wQi8nyGhsbg9Fo5L6qcblcXD2Jeb74xPfs2bOg1z4x1lK0hKov\n6vI1Bfx+P+rq6jA0NMS1HM3IyAhqVQoAKpUKDoeDG9ff3w+r1QqZTAaJRILGxkYsWLBAqDT+wiev\n06dPw+l0QiqVQqvVoqqqKuw4sYiUV3JyMkpLS4N2QioqKqBUKnHo0CHuTYpYfnpjs9nQ3d0NiUQC\nq9WKvr4+zJs3D8XFxXC5XNwOzoYNG2A0GkOO+f0FVSzC5VVQUBBUVwCwZcsWbN68GRaLBV+/foXP\n50N1dTX3XbeYTDZfly5dwvXr1zFr1iwsX74c9fX1kEgkop+vyXICgK1bt6KtrQ1paWkAAjtZYqyl\nP/36ue7w8DBkMhkUCgW0Wi0yMzMFqy9atAkhhJA4QdvjhBBCSJygRZsQQgiJE7RoE0IIIXGCFm1C\nCCEkTtCiTQghhMQJWrQJIYSQOEGLNiGEEBInaNEmhBBC4sR/Se9sx+a7q3gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6712df9198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2XImjkyN1MZn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Convert to PyTorch tensors\n",
        "X = torch.from_numpy(X).float()\n",
        "y = torch.from_numpy(y).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gGFqcqTDXhkl",
        "colab_type": "code",
        "outputId": "4b9f6fd1-3d85-4015-d53f-eb89daad04a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Shuffle data\n",
        "shuffle_indicies = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indicies]\n",
        "y = y[shuffle_indicies]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 1125 train samples and 375 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IHofozO7RIiV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Linear model"
      ]
    },
    {
      "metadata": {
        "id": "DlVmr5XkRMCf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Before we get to our neural network, we're going to implement a linear model (logistic regression) in PyTorch first. We want to see why linear models won't suffice for our dataset."
      ]
    },
    {
      "metadata": {
        "id": "qja6kvBrRKDj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from tqdm import tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5AdXiZ8ORKGS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Linear model\n",
        "class LogisticClassifier(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(LogisticClassifier, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = self.fc1(x_in)\n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ApoN49qmRhl5",
        "colab_type": "code",
        "outputId": "0d06b9d4-3a94-4c45-81a1-d6d302734714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = LogisticClassifier(input_dim=args.dimensions, \n",
        "                           hidden_dim=args.num_hidden_units, \n",
        "                           output_dim=args.num_classes)\n",
        "print (model.named_modules)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Module.named_modules of LogisticClassifier(\n",
            "  (fc1): Linear(in_features=2, out_features=100, bias=True)\n",
            "  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",
            ")>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ksXe8ruRhoh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) # Adam optimizer (usually better than SGD)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bt3kwn_3kso9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Accuracy\n",
        "def get_accuracy(y_pred, y_target):\n",
        "    n_correct = torch.eq(y_pred, y_target).sum().item()\n",
        "    accuracy = n_correct / len(y_pred) * 100\n",
        "    return accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cpo8c46ERhrS",
        "colab_type": "code",
        "outputId": "06927f18-a3f3-4738-e94c-32b32fc3fd45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | acc: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 1.1694 | acc: 32.6%\n",
            "epoch: 20 | loss: 14.5043 | acc: 55.1%\n",
            "epoch: 40 | loss: 3.9626 | acc: 54.1%\n",
            "epoch: 60 | loss: 1.3633 | acc: 54.0%\n",
            "epoch: 80 | loss: 0.7533 | acc: 54.5%\n",
            "epoch: 100 | loss: 0.7478 | acc: 55.6%\n",
            "epoch: 120 | loss: 0.7330 | acc: 54.2%\n",
            "epoch: 140 | loss: 0.7329 | acc: 54.0%\n",
            "epoch: 160 | loss: 0.7327 | acc: 53.8%\n",
            "epoch: 180 | loss: 0.7327 | acc: 53.8%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZthV18sPRhto",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZjKHD3zXbb0I",
        "colab_type": "code",
        "outputId": "f6e04324-9582-4a5f-f7c9-fe5e44181126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 53.8%, test acc: 53.1%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7hsn8zbxRh09",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "def plot_multiclass_decision_boundary(model, X, y):\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n",
        "    \n",
        "    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n",
        "    y_pred = model(X_test, apply_softmax=True)\n",
        "    _, y_pred = y_pred.max(dim=1)\n",
        "    y_pred = y_pred.reshape(xx.shape)\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5u5fEhOcRh3V",
        "colab_type": "code",
        "outputId": "9ebec4e7-f6f1-487c-b451-9493ffd138d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z3PdtJakJAPd/N4cP/34f3vHWqgznK695eGGv29bxfvZsCY8+UcCOi/TgJ42ml1jzHlbQ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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670ecf3e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "llXUlx5bRKJB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NC9Hqrl7RxVr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix\n",
        "def plot_confusion_matrix(cm, classes):\n",
        "    cmap=plt.cm.Blues\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(\"Confusion Matrix\")\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    plt.grid(False)\n",
        "\n",
        "    fmt = 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", \n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tlwsKnBhRxYH",
        "colab_type": "code",
        "outputId": "26622315-45e5-44dc-8a2c-0896289eae18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, pred_test)\n",
        "plot_confusion_matrix(cm=cm, classes=[0, 1, 2])\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.50      0.50      0.50       125\n",
            "          1       0.55      0.55      0.55       117\n",
            "          2       0.54      0.55      0.54       133\n",
            "\n",
            "avg / total       0.53      0.53      0.53       375\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dw0Nr1qzR9u3bNWrUKNWpU0cTJ05UVlaWMjMz9a9//UstW7bU/v37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\nwyKkAACGRUgBAAzr/wE9cEncTDDlIgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670ececef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "br_O_NUCjkwm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Non-linear model"
      ]
    },
    {
      "metadata": {
        "id": "YexKO17makzj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now let's see how the MLP performs on the data. Note that the only difference is the addition of the non-linear activation function (we use ReLU which is just $max(0, z))$. "
      ]
    },
    {
      "metadata": {
        "id": "-U0_3Xco0Q4g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = F.relu(self.fc1(x_in)) # activaton function added!\n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V-8S6w1njkDC",
        "colab_type": "code",
        "outputId": "5ed4037a-7116-4f86-c989-906a8605bad2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=args.dimensions, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=args.num_classes)\n",
        "print (model.named_modules)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Module.named_modules of MLP(\n",
            "  (fc1): Linear(in_features=2, out_features=100, bias=True)\n",
            "  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",
            ")>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YtZvQVVpvl5p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nmeU45XF0O-U",
        "outputId": "90bf9112-a477-437a-d65a-b59349498b20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | acc: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 1.1470 | acc: 23.7%\n",
            "epoch: 20 | loss: 0.5179 | acc: 71.6%\n",
            "epoch: 40 | loss: 0.2400 | acc: 87.8%\n",
            "epoch: 60 | loss: 0.1402 | acc: 93.3%\n",
            "epoch: 80 | loss: 0.0560 | acc: 98.9%\n",
            "epoch: 100 | loss: 0.0344 | acc: 99.2%\n",
            "epoch: 120 | loss: 0.0283 | acc: 99.6%\n",
            "epoch: 140 | loss: 0.0241 | acc: 99.7%\n",
            "epoch: 160 | loss: 0.0215 | acc: 99.8%\n",
            "epoch: 180 | loss: 0.0197 | acc: 99.8%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y4tI2iZ1vl8e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CgM4qu8I62BP",
        "colab_type": "code",
        "outputId": "b38195c6-4b00-44b8-9ed0-3e07657ab1e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 99.8%, test acc: 99.7%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Xj7cwzZ4NoVI",
        "colab_type": "code",
        "outputId": "cdee010c-f955-4632-feda-c4038e0b9baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qSI85GD88DL/GsXg53+KfDu/NJKpqlJdqwInNrjie+lS/67bRj1LVjCYlJOLL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CTYDHZJ68lELG1hC9sOwtMBzF9pLR94GH3mzL1Vd7IsCu0LsVPaEfGzNQO2YUJUlbZf0y2qmYpr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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670d4812b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IsxhNLTr_Jg7",
        "colab_type": "code",
        "outputId": "5e44b5b5-c6fd-4c2d-bfdd-1914734e33c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, pred_test)\n",
        "plot_confusion_matrix(cm=cm, classes=[0, 1, 2])\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.99      1.00      1.00       125\n",
            "          1       1.00      0.99      1.00       117\n",
            "          2       1.00      1.00      1.00       133\n",
            "\n",
            "avg / total       1.00      1.00      1.00       375\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5+Pho/vz5CgsLc+zbvn277r//fklSp06dlJSUpL1796pZs2aqXLmyfH191bJlS+3atavY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\nAQCMQ3gBAIxDeAEAjEN4AQCM8/8B3IoY9/KCn6MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670d49bdd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "P-TgqRKOOSUw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Visualizing weights"
      ]
    },
    {
      "metadata": {
        "id": "H7F2Ye5hOSYr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So far, we've seen metrics like loss and accuracy and even visualized decision boundaries. But what about our weights? It's a little tricky to visualize them because there are so many and they are constantly all updated. But this is crucial because our weights can casue many downstream issues such as all the weights being nearly zero or weights quickly growing in magnitude. Both of these issues indicate that our model needs some finetuning/normalization but we need to be able to see our weights to discern this. Since the weights can have large dimensions, we can also visualize the mean and std of the different parameters.\n",
        "\n",
        "To visualize everything, we will use [Tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard) with PyTorch. Tensorboard allows us to visualize on a localhost but it's a little bit tricky with our Google colab notebook so we're going to use a localtunnel to expose this notebook's webserver. If you're doing this on your local machine, you can just run `tensorboard --logdir='./logs' --port=6006` in the terminal and open TensorBoard at: `http://localhost:6006`. \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/tensorboard.png\" width=600>"
      ]
    },
    {
      "metadata": {
        "id": "j7GfEoEWhExI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here are a list of things that are good to measure and visualize:\n",
        "1. loss and accuracy\n",
        "2. weight means and stds\n",
        "3. activation means and stds\n",
        "4. gradient means and stds"
      ]
    },
    {
      "metadata": {
        "id": "jilLRr8W05qC",
        "colab_type": "code",
        "outputId": "e5d9238b-7644-41d4-e229-620afdcbc935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "# Install TensorboardX\n",
        "!pip3 install tensorboardX"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorboardX\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b1/d2/e08fe62f3554fbba081e80f6b23128df53b2f74ed4dcde73ec4a84dc53fb/tensorboardX-1.4-py2.py3-none-any.whl (67kB)\n",
            "\r\u001b[K    15% |████▉                           | 10kB 18.5MB/s eta 0:00:01\r\u001b[K    30% |█████████▊                      | 20kB 1.8MB/s eta 0:00:01\r\u001b[K    45% |██████████████▋                 | 30kB 2.6MB/s eta 0:00:01\r\u001b[K    60% |███████████████████▌            | 40kB 1.7MB/s eta 0:00:01\r\u001b[K    76% |████████████████████████▍       | 51kB 2.1MB/s eta 0:00:01\r\u001b[K    91% |█████████████████████████████▎  | 61kB 2.5MB/s eta 0:00:01\r\u001b[K    100% |████████████████████████████████| 71kB 2.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.14.6)\n",
            "Requirement already satisfied: protobuf>=3.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (3.6.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.11.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.2.0->tensorboardX) (40.6.2)\n",
            "Installing collected packages: tensorboardX\n",
            "Successfully installed tensorboardX-1.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "t6rsfwPuRRMC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Run tensorboard on port 6006\n",
        "LOG_DIR = './log'\n",
        "run_num = 0\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FwlrVIikRhzt",
        "colab_type": "code",
        "outputId": "9f81084b-170a-4aff-d289-35bf660f3e4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# Install localtunnel\n",
        "!npm install -g localtunnel"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/client\n",
            "+ localtunnel@1.9.1\n",
            "added 54 packages from 32 contributors in 3.207s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pG64Zj7gRjz2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Tunnel port 6006 for tensorboard\n",
        "get_ipython().system_raw('lt --port 6006 >> tensorboard.txt 2>&1 &')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "efEPZIuSQJKP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now let's train our model and see some visualizations on our tensorboard."
      ]
    },
    {
      "metadata": {
        "id": "Q4L_Um9MP3EW",
        "colab_type": "code",
        "outputId": "84617b02-a1d5-4d4a-c4d0-a125455965c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "cell_type": "code",
      "source": [
        "# Few things needed to get tensorboard working\n",
        "from tensorboardX import SummaryWriter\n",
        "import torchvision.utils as vutils\n",
        "!pip install Pillow==4.0.0\n",
        "!pip install PIL\n",
        "!pip install image\n",
        "from PIL import Image\n",
        "def register_extension(id, extension): Image.EXTENSION[extension.lower()] = id.upper()\n",
        "Image.register_extension = register_extension\n",
        "def register_extensions(id, extensions): \n",
        "    for extension in extensions: register_extension(id, extension)\n",
        "Image.register_extensions = register_extensions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting Pillow==4.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/e8/b3fbf87b0188d22246678f8cd61e23e31caa1769ebc06f1664e2e5fe8a17/Pillow-4.0.0-cp36-cp36m-manylinux1_x86_64.whl (5.6MB)\n",
            "\u001b[K    100% |████████████████████████████████| 5.6MB 5.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow==4.0.0) (0.46)\n",
            "\u001b[31mtorchvision 0.2.1 has requirement pillow>=4.1.1, but you'll have pillow 4.0.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: Pillow\n",
            "  Found existing installation: Pillow 5.3.0\n",
            "    Uninstalling Pillow-5.3.0:\n",
            "      Successfully uninstalled Pillow-5.3.0\n",
            "Successfully installed Pillow-4.0.0\n",
            "Collecting PIL\n",
            "\u001b[31m  Could not find a version that satisfies the requirement PIL (from versions: )\u001b[0m\n",
            "\u001b[31mNo matching distribution found for PIL\u001b[0m\n",
            "Collecting image\n",
            "  Downloading https://files.pythonhosted.org/packages/0c/ec/51969468a8b87f631cc0e60a6bf1e5f6eec8ef3fd2ee45dc760d5a93b82a/image-1.5.27-py2.py3-none-any.whl\n",
            "Collecting django (from image)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fd/9a/0c028ea0fe4f5803dda1a7afabeed958d0c8b79b0fe762ffbf728db3b90d/Django-2.1.4-py3-none-any.whl (7.3MB)\n",
            "\u001b[K    100% |████████████████████████████████| 7.3MB 4.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from image) (4.0.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from django->image) (2018.7)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->image) (0.46)\n",
            "Installing collected packages: django, image\n",
            "Successfully installed django-2.1.4 image-1.5.27\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HUsGMQliVf6j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the Tensorboard writer\n",
        "run_num += 1\n",
        "writer = SummaryWriter(log_dir=LOG_DIR+\"/run_{}\".format(run_num))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VGLYVpplWTqK",
        "colab_type": "code",
        "outputId": "69a02442-17b5-4131-c9c2-1aa65ad312e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=args.dimensions, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=args.num_classes)\n",
        "print (model.named_modules)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Module.named_modules of MLP(\n",
            "  (fc1): Linear(in_features=2, out_features=100, bias=True)\n",
            "  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",
            ")>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "G8hWwoCUWTwG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DshX8a8qaa1q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        " def write_weights(writer, model, epoch_num):\n",
        "    for name, param in model.named_parameters():\n",
        "        \n",
        "        # Weights\n",
        "        writer.add_scalar(name+\"/mean\", param.data.numpy().mean(), epoch_num)\n",
        "        writer.add_scalar(name+\"/std\", param.data.numpy().std(), epoch_num)\n",
        "        \n",
        "        # Gradients\n",
        "        writer.add_scalar(name+\"/grad_mean\", torch.mean(param.grad), epoch_num)\n",
        "        writer.add_scalar(name+\"/grad_std\", torch.std(param.grad), epoch_num)\n",
        "        \n",
        "        # Weights histogram (dim over 1024 cause an error)\n",
        "        if len(param.size()) > 1 and param.size()[-1] <= 1024: \n",
        "            writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch_num)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A336qkQxWTuP",
        "colab_type": "code",
        "outputId": "8fd6624d-bdd6-47e0-a5ba-fa7bd3584167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()\n",
        "    \n",
        "    # Write to tensorboard\n",
        "    writer.add_scalar('metrics/train_loss', loss, t)\n",
        "    writer.add_scalar('metrics/train_acc', accuracy, t)\n",
        "    writer.add_scalar('metrics/lr', optimizer.param_groups[0]['lr'], t)\n",
        "    write_weights(writer=writer, model=model, epoch_num=t)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 1.1047 | accuracy: 34.0%\n",
            "epoch: 20 | loss: 0.7275 | accuracy: 56.0%\n",
            "epoch: 40 | loss: 0.3969 | accuracy: 79.3%\n",
            "epoch: 60 | loss: 0.2519 | accuracy: 82.0%\n",
            "epoch: 80 | loss: 0.1990 | accuracy: 86.0%\n",
            "epoch: 100 | loss: 0.1586 | accuracy: 90.7%\n",
            "epoch: 120 | loss: 0.1289 | accuracy: 92.9%\n",
            "epoch: 140 | loss: 0.1018 | accuracy: 95.2%\n",
            "epoch: 160 | loss: 0.0806 | accuracy: 96.4%\n",
            "epoch: 180 | loss: 0.0467 | accuracy: 98.7%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j-IEhlPYVf38",
        "colab_type": "code",
        "outputId": "39b42566-7d01-4f1e-9f5c-31334cf4183a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "print (\"Go to this link below to see the Tensorboard:\")\n",
        "!cat tensorboard.txt\n",
        "print (\"Click on SCALARS to see metrics and DISTRIBUTIONS to see weights.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this link below to see the Tensorboard:\n",
            "your url is: https://tame-puma-78.localtunnel.me\n",
            "Click on SCALARS to see metrics and DISTRIBUTIONS to see weights.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R3OK8p-Ng3BC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Activation functions"
      ]
    },
    {
      "metadata": {
        "id": "ghf5uLuhg3D0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In our MLP, we used the ReLU activation function ($max(0,z)$) which is by far the most widely use option. But there are several other options for activation functions as well, each with their own unique properties. "
      ]
    },
    {
      "metadata": {
        "id": "ivnfSKEhg3Md",
        "colab_type": "code",
        "outputId": "45b7cb4b-a87c-4bf5-dd81-4c3b1ef381ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "cell_type": "code",
      "source": [
        "# Fig size\n",
        "plt.figure(figsize=(12,3))\n",
        "\n",
        "# Data\n",
        "x = torch.arange(-5., 5., 0.1)\n",
        "\n",
        "# Sigmoid activation (constrain a value between 0 and 1.)\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.title(\"Sigmoid activation\")\n",
        "y = torch.sigmoid(x)\n",
        "plt.plot(x.numpy(), y.numpy())\n",
        "\n",
        "# Tanh activation (constrain a value between -1 and 1.)\n",
        "plt.subplot(1, 3, 2)\n",
        "y = torch.tanh(x)\n",
        "plt.title(\"Tanh activation\")\n",
        "plt.plot(x.numpy(), y.numpy())\n",
        "\n",
        "# Relu (clip the negative values to 0)\n",
        "plt.subplot(1, 3, 3)\n",
        "y = F.relu(x)\n",
        "plt.title(\"ReLU activation\")\n",
        "plt.plot(x.numpy(), y.numpy())\n",
        "\n",
        "# Show plots\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x8nRTO44QQth9u+0kVUYz06/rRagDBsOdS4pg4TBVxgbeWHaA2noLv5w2kOED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\nIiIiIpJ1/g/ylg2C5AOf5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670d346a58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gWv-RU46k2UL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Initializing weights"
      ]
    },
    {
      "metadata": {
        "id": "1hDPBE0sk2mJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So far we have been initializing weights with small random values and this isn't optimal for convergence during training. The objective is to have weights that are able to produce outputs that follow a similar distribution across all neurons. We can do this by enforcing weights to have unit variance prior the affine and non-linear operations.\n",
        "\n",
        "A popular method is to apply [xavier initialization](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization), which essentially initializes the weights to allow the signal from the data to each deep into the network. We're going to use this in our models with PyTorch.\n",
        "\n",
        "You may be wondering why we don't do this for every forward pass and that's a great question. We'll look at more advanced strategies that help with optimization like batch/layer normalization, etc. in future lessons."
      ]
    },
    {
      "metadata": {
        "id": "SOYptFo7k-JI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def init_weights(self):\n",
        "        init.xavier_normal(self.fc1.weight, gain=nn.init.calculate_gain('relu')) \n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = F.relu(self.fc1(x_in)) # activaton function added!\n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9ijvHwcZg8mO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Overfitting"
      ]
    },
    {
      "metadata": {
        "id": "FIhvdD_zg8os",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Though neural networks are great at capturing non-linear relationships they are highly susceptible to overfitting to the training data and failing to generalize on test data. Just take a look at the example below where we generate completely random data and are able to fit a model with [$2*N*C + D$](https://arxiv.org/abs/1611.03530) hidden units. The training performance is great but the overfitting leads to very poor test performance. We'll be covering strategies to tackle overfitting in future lessons."
      ]
    },
    {
      "metadata": {
        "id": "uRdM16NhazJP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Arguments\n",
        "args = Namespace(\n",
        "    seed=1234,\n",
        "    num_samples_per_class=40,\n",
        "    dimensions=2,\n",
        "    num_classes=3,\n",
        "    train_size=0.75,\n",
        "    test_size=0.25,\n",
        "    num_hidden_units=2*40*3+2 , # 2*N*C + D\n",
        "    learning_rate=1e-3,\n",
        "    regularization=1e-3,\n",
        "    num_epochs=1000,\n",
        ")\n",
        "\n",
        "# Set seed for reproducability\n",
        "np.random.seed(args.seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qf00Biq6g8ty",
        "colab_type": "code",
        "outputId": "fb7c0f5c-b15e-402e-ebce-a0260118aaea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Generate random data\n",
        "X = torch.randn(args.num_samples_per_class*args.num_classes, args.dimensions).float()\n",
        "y = torch.LongTensor([[i]*args.num_samples_per_class \n",
        "                       for i in range(args.num_classes)]).view(-1)\n",
        "print (\"X: {0}\".format(np.shape(X)))\n",
        "print (\"y: {0}\".format(np.shape(y)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: torch.Size([120, 2])\n",
            "y: torch.Size([120])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-bA9vK9SWkjh",
        "colab_type": "code",
        "outputId": "4d304402-2504-4c3d-9388-5260c0868df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Shuffle data\n",
        "shuffle_indicies = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indicies]\n",
        "y = y[shuffle_indicies]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 90 train samples and 30 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w-_8b7AlaFdY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MLP, self).__init__()\n",
        "        print \n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def init_weights(self):\n",
        "        init.xavier_normal(self.fc1.weight, gain=nn.init.calculate_gain('relu'))\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = F.relu(self.fc1(x_in)) \n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xozz2bBoWkmq",
        "colab_type": "code",
        "outputId": "be6b5de8-f06e-4407-c872-5b5fd3b6d164",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=args.dimensions, hidden_dim=args.num_hidden_units, \n",
        "            output_dim=args.num_classes)\n",
        "print (model.named_modules)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Module.named_modules of MLP(\n",
            "  (fc1): Linear(in_features=2, out_features=242, bias=True)\n",
            "  (fc2): Linear(in_features=242, out_features=3, bias=True)\n",
            ")>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bXnkWoPaWkpe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zgayj4E1WksH",
        "colab_type": "code",
        "outputId": "ebba47f1-25de-4a51-f6d8-cda697c500ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "\n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%100==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 1.1201 | accuracy: 35.6%\n",
            "epoch: 100 | loss: 0.9448 | accuracy: 51.1%\n",
            "epoch: 200 | loss: 0.8145 | accuracy: 66.7%\n",
            "epoch: 300 | loss: 0.7117 | accuracy: 72.2%\n",
            "epoch: 400 | loss: 0.6309 | accuracy: 76.7%\n",
            "epoch: 500 | loss: 0.5663 | accuracy: 80.0%\n",
            "epoch: 600 | loss: 0.5146 | accuracy: 82.2%\n",
            "epoch: 700 | loss: 0.4734 | accuracy: 82.2%\n",
            "epoch: 800 | loss: 0.4378 | accuracy: 83.3%\n",
            "epoch: 900 | loss: 0.4063 | accuracy: 85.6%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "p3OJLNwuZxtk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_LU9Wzt0ZxwI",
        "colab_type": "code",
        "outputId": "0586e063-e940-495b-b872-ace35455a4cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 87.8%, test acc: 43.3%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rpSoAEdGWku5",
        "colab_type": "code",
        "outputId": "85df95a1-c511-407b-f28e-e92cbc08a214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0/ftpbJ+px3pZefSYf9xH97/3UPLhADaOYWhdj16Ln6n3WD3RmeJIKK41Lzyo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/k2YS21E18yUydGCb38esHHcvHMEL97tp8rb4xx8fQQb57oZ27SW/DjCASC8iXS5e5QZxDtxdOl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\nKB6jo/APD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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670d1f0358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_4m9TXpTZ22C",
        "colab_type": "code",
        "outputId": "bab12967-1565-49e0-b139-06d67b803fed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, pred_test)\n",
        "plot_confusion_matrix(cm=cm, classes=[0, 1, 2])\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "          0       0.47      0.73      0.57        11\n",
            "          1       0.57      0.29      0.38        14\n",
            "          2       0.17      0.20      0.18         5\n",
            "\n",
            "avg / total       0.47      0.43      0.42        30\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ew/KITQ0AADPQIQGATTFlBwAwgmF5RCABgF3RIblwfZce3i4BZbDkh2PeLgG4Zo3scINH\njmNYHrGpAQBgBmM6JACAZzFlBwAwgmF5RCABgF2Z1iGxhgQAMAIdEgDYlGENEoEEAHZl2pQdgQQA\nNkUgAQCMYFgesakBAGAGOiQAsCmm7AAARjAsjwgkALArOiQAgBEMyyM2NQAAzECHBAA25WNYi0Qg\nAYBNGZZHBBIA2BWbGgAARvAxK4/Y1AAAMAMdEgDYFFN2AAAjGJZHBBIA2JVDZiUSgQQANsWmBgAA\nikCHBAA2xaYGAIARDMsjAgkA7Mrqa9lt2rRJL7zwgm688UZJ0k033aQxY8a4HE8gAYBNeaJD+v3v\nf69p06aVaiybGgAARnDZISUkJBT7wl69erm9GACA53hiU8P+/fs1aNAgnTlzRkOHDlXbtm1djnUZ\nSFu3bi32IAQSAJRvVudRo0aNNHToUEVFRenw4cN6/PHHtWbNGgUEBBQ53mUgvf766wU/5+fnKz09\nXTVr1nR/xQAAr7B6U0NYWJi6dOkiSWrQoIFq1Kih1NRU1a9fv+h6SnrD5ORkderUSTExMZKkyZMn\nKykpyX0VAwCuSYmJiZo7d64kKS0tTenp6QoLC3M5vsRAmjp1qj777LOC7mjQoEF655133FQuAMBb\nHG64Fadjx4769ttv1a9fPw0ZMkTjx493OV0nlWLbd6VKlVSjRo2C+yEhIfL39y/pZQAAw1m9qSEo\nKEizZ88u9fgSA6lixYravHmzJOnMmTNavny5KlSo8NsrBAAYodxdXHXcuHGaO3eudu7cqc6dO2v9\n+vV69dVXPVEbAMBCDofjqm/uVGKHVKdOHc2ZM8etBwUA4NdK7JC+/fZb9ezZUy1btlRERIT69u1b\n4jlKAADzORxXf3OnEjukV199VaNGjdJtt90mp9OprVu3asKECUpMTHRvJQAAjyp3Xz8RGhqq1q1b\nF9xv27at6tata2lRAADrmbapwWUgHT58WJJ0yy23aN68eWrTpo18fHyUnJyspk2beqxAAIA1yk2H\n9Mc//lEOh0NOp1OS9OGHHxY853A49Pzzz1tfHQDANlwG0j//+U+XL9q2bZslxQAAPMes/qgUa0jn\nz5/X0qVLlZGRIUnKycnR4sWLtWHDBsuLK89qVqmgyb2aq2FoJZ3PztWkf+zV1v9leLssFGPvxnVa\nMOoZ/fnjJIXUruftclACPq+rZ/XFVcuqxEAaNmyY6tatqw0bNigyMlLffPONxo8f74HSyrfJvZpr\nw76TeuabQ/r9dcHq16o+gWSwSxeztPLdv6hS1ereLgWlwOflHoblUcnnIWVnZ+vVV19VeHi4Ro4c\nqQ8++EArV670RG3lVu1qFdSsblV9lPyTJGnzwQwNX7jDy1WhOGvfn6bbOj+ogMDK3i4FpcDn5R6m\nXamhxEDKyclRZmam8vPzlZGRoerVqxfswEPRbq5dRSkZWXox8kYtG9ZWC56+Q7+rU8XbZcGF4z/+\nR//dukF393rS26WgFPi8rl0lBtIf/vAHffbZZ+rdu7e6dOmirl27KjQ01BO1lVtVKvrrprAgbflf\nhrr97Rst++6Y3u7XUr6mbfqHnE6nPp86Rn94bpx8/biKven4vNyr3F2p4ZFHHin4uXXr1kpPTy/1\neUj79u3TkCFD9MQTT+ixxx777VWWM+ezc3Xy/CWt25MmSUrYckQvRd2kRqGVdCDtgperwy9tXrZQ\ntRo2VqNb7vB2KSgFPi/3KjebGt5++22XL/ryyy/1wgsvFPvGmZmZmjhxYqGrPNjF0YwsVa7gK4dD\n+r/TuJTvlPJ+vgNj/PDNWqXs26k9PS+f5nDhzCnNHPyQ+o2dphsi7Pf/XdPxebmXYXnkOpB8fX2v\n6o0DAgL07rvv6t13372q9ymP9qWeV9q5bPW6I1yLvj2i+5uH6WxWjg6fyvJ2afiVJ9+YW+j+G4+0\n04CpH7GN2FB8Xu5Vbq7UMHTo0Kt7Yz8/+fmVOCN4zXrx4+81qVdz9b/3Op06f0l/+uR75eXTIQGA\nK/ZNDIsdSLug6FmbvF0Gyij2k6+9XQLKgM/r6pS4q83DCCQAsCnTpuxKFZAZGRnauXOnJCk/P9/S\nggAAnuHjuPqbO5XYIS1btkzTpk1TQECAli1bpokTJ6pp06bq3bt3sa/btWuX4uLidOTIEfn5+Wn1\n6tWaPn26qlfnUh8AgCuVGEjz58/X0qVLNWDAAEnSyJEjFRMTU2IgNW/eXPHx8e6pEgDgdqadq19i\nIFWpUkWBgYEF9ytWrCh/f86QBoDyzrQ1pBIDKTg4WJ9//rmys7O1e/durVixQiEhIZ6oDQBgIdM6\npBI3NUyYMEE7d+7UhQsXNHr0aGVnZ+u1117zRG0AAAuVu2vZVa1aVWPHjnXvUQEA+JUSA6ldu3ZF\nzjMmJSVZUQ8AwEPKzcVVf/bxxx8X/JyTk6Pk5GRlZ2dbWhQAwHrl7koN4eHhhe43atRI/fv31xNP\nPGFVTQAADzCsQSo5kJKTkwvdP378uH766SfLCgIAeEa5m7J75513Cn52OBwKCgrShAkTLC0KAGA/\nJQZSbGysmjVr5olaAAAeZFiDVPKaVlxcnCfqAAB4WLm7uGrdunUVExOjW2+9tdAlg0r6CnMAgNnK\n3RpSvXr1VK8eXw8MANcaw/LIdSAlJiaqR48eV/1V5gAAlIbLNaSEhARP1gEA8DBPrSFdvHhRnTp1\n0pIlS4odx1eYA4BNOeSZObtZs2apWrVqJY5zGUjbt29X+/btr3jc6XTK4XBwLTsAKOc88fUTBw4c\n0P79+4vMk19zGUhNmzbVX//6V3fWBQAwiCcCKS4uTmPGjNEXX3xR4liXgRQQEHDFdewAACitL774\nQi1btlT9+vVLNd5lILVo0cJtRQEAzGP1V5gnJSXp8OHDSkpK0vHjxxUQEKDatWurTZs2RY53GUgj\nRoywrEgAgPdZPWX3t7/9reDn6dOnKzw83GUYSeyyAwDbKjcnxgIArm2evHTQc889V+IY074wEABg\nU3RIAGBTntj2XRYEEgDYFGtIAAAj+Hjo0kGlxRoSAMAIdEgAYFNM2QEAjMCmBgCAEcrdV5gDAK5N\nhuURmxoAAGagQwIAm2LKDgBgBMPyiEACALsybc2GQAIAm7L6C/rKyrSABADYFB0SANiUWf0RgQQA\ntsUuOwCAEcyKIwIJAGzLsAaJTQ0AADPQIQGATZm27ZtAAgCbMm2KjEACAJuiQwIAGMGsODKvYwMA\n2JQxHdLfHovwdgkog3turOntEgBcJabsAABGMG2KjEACAJsyrUMyLSABADZFhwQANmVWf0QgAYBt\nGTZjRyABgF35GNYjEUgAYFOmdUhsagAAGIEOCQBsysGUHQDABKZN2RFIAGBTbGoAABjB6g4pKytL\nsbGxSk9PV3Z2toYMGaIOHTq4HE8gAQAssW7dOjVv3lzPPPOMjhw5oqeeeopAAgBcyeoOqUuXLgU/\nHzt2TGFhYcWOJ5AAwKY8tcsuOjpax48f1+zZs4sdRyABgE35eGhPw8KFC7Vnzx6NGDFCiYmJLq8y\nzomxAGBTDjf8K86uXbt07NgxSVKTJk2Ul5enU6dOuRxPIAEALLFlyxbNmzdPknTy5EllZmYqODjY\n5XgCCQBsyuG4+ltxoqOjderUKfXr108DBgzQ2LFj5ePjOnZYQwIAm7J6U0PFihX11ltvlXo8gQQA\nNuWpTQ2lRSABgE2ZdnFV1pAAAEagQwIAm+Jq3wAAIxiWRwQSANiVj2EtEoEEADZlVhyxqQEAYAg6\nJACwK8NaJAIJAGzKtPOQCCQAsCnD9jSwhgQAMAMdEgDYlGENEoEEALZlWCIRSABgU2xqAAAYgU0N\nAAAUgQ4JAGzKsAaJQAIA2zIskQgkALApNjUAAIzApgYAAIpAhwQANmVYg0QgAYBtGZZITNlZZP2a\nf2jQQx30VNc2evGxbjr43z3eLgnFyMnJ0cgRwxXo71BKSoq3y0EJ+Lzcw+GGf+5EIFngxNEUTZvw\nZ02Y8YHmLf+37o3sob+OHubtslCM3g//QUFBQd4uA6XE5+UeDsfV39yJQLKAr7+/Yv8yS2Hh9SVJ\nLVvdo8MH93u5KhQndtQYjRk3wdtloJT4vK5NrCFZILRmmEJrhkmS8nJz9eXnC9Wm4wNergrFadW6\ntbdLQBnwebmHYUtIdEhW+jz+7+pzTzPt3LpJ/YeP9XY5AFCYww03N7I0kKZMmaK+ffuqZ8+eWrNm\njZWHMtJDMQOU8O+9evjxARrWr6uyL2Z5uyQAKGCbTQ0bN27Uf//7X3366ad67733NHnyZKsOZZyf\nDuzTtn9/LUlyOBzq0PVhZZ4/pxTWkQAYxDabGu688069/fbbkqSqVasqKytLeXl5Vh3OKKcz0jXl\n5aFKP3FckrR72ybl5eaodv1G3i0MAAxm2aYGX19fVapUSZKUkJCge++9V76+vlYdzigt7mitfgOH\naeRTvZTvzFdAQIBGvfl3VQ6q4u3SUITU1FTdf1+7gvuRndrLz89PK1Z/pfDwcC9WhqLwebmPaZsa\nHE6n02nlAdauXas5c+Zo3rx5qlLF9R/kL/ekWVkG3OyeG2t6uwTgmlXRQ/uf9xy7cNXv0aROZTdU\ncpmlv/b69es1e/Zsvffee8WGEQDA82zz9RPnzp3TlClTtGDBAlWvXt2qwwAArhGWBdKKFSuUkZGh\nYcP+/yVz4uLiVLduXasOCQAoA9O+D8nyNaTSYg2pfGENCbCOp9aQ9h3PvOr3uKl2pWKfnzJlirZu\n3arc3FwNHDhQ999/v8uxXDoIAOzK4g7pl+ejZmRk6KGHHiKQAABXsnpTw5133qkWLVpIKnw+qqtT\ngLiWHQDAEmU9H5UOCQBsylObGtauXauEhATNmzev2HEEEgDYlCfyqCznoxJIAGBXFidSWc9HJZAA\nwKas3tRQ1vNROQ8JvwnnIQHW8dR5SD+mXbzq97i+ZkU3VHIZHRIA2JRpV2ogkADApgzLIwIJAGzL\nsEQikADApkz7+gmu1AAAMAIdEgDYFJsaAABGMCyPCCQAsCs6JACAIcxKJDY1AACMQIcEADbFlB0A\nwAiG5RGBBAB2RYcEADACV2oAAKAIdEgAYFdmNUgEEgDYlWF5RCABgF2ZtqmBNSQAgBHokADApkzb\nZUcgAYBdmZVHBBIA2JVheUQgAYBdsakBAIAi0CEBgE2xqQEAYASm7AAAKAIdEgDYFB0SAABFoEMC\nAJtiUwMAwAimTdkRSABgU4blEYEEALZlWCKxqQEAYAQ6JACwKTY1AACMwKYGAIARDMsj1pAAwLYc\nbriVYN++ferUqZM+/PDDEscSSAAAS2RmZmrixIlq3bp1qcYTSABgUw43/CtOQECA3n33XdWqVatU\n9bCGBAA2ZfWmBj8/P/n5lT5mjAmkzk1qersEALCVisYkwGVM2QEAjEAgAQCM4HA6nU5vFwEAuPbs\n2rVLcXFxOnLkiPz8/BQWFqbp06erevXqRY4nkAAARmDKDgBgBAIJAGAEAgkAYAQCyc3OnDmjc+fO\nebsMlFJeXp63S0AZnDhxQocPH/Z2GbCIYadFlW9ff/11wWUyQkJCNHr0aG+XhGJs3rxZBw8eVOfO\nnRUSEuLtclCCpKQkzZo1S4GBgapRo4befPNNb5cEN6NDcpOUlBQtWLBAY8aM0aRJk3Tw4EFNnDhR\nGRkZ3i4NLsTHx2vjxo1au3atTp065e1yUIzjx48rPj5eU6ZM0YIFC/Tjjz+W6urRKF8IJDcJDAyU\nr6+v/P39FRgYqNmzZ+vcuXOaNm2at0uDCxUqVFDt2rV14MABrVmzhlAymL+/v7Kzs+Xjc/lP1jPP\nPKPc3FwvVwV38x0/fvx4bxdxLahYsaJSU1OVkZGhsLAwValSRR06dND8+fP1n//8R/fcc4+3S8Sv\nNG/eXFFRUbp06ZJ++OEHnTx5UuHh4QoMDJTT6ZTDtK/TtDF/f3/Vq1dPzZo1kyTt379fGzduVGRk\npCQpNze3IKxQfvEJuomPj48eeOABff/999q8ebNOnDghPz8/TZ06VZmZmfzXnIFq164tSbrvvvsU\nERGh//3vf9q4caM++ugjffDBB16uDr/k7+9f6Dt1KlasKF9fX0nSF198oXnz5olz/Ms/NjW4UYMG\nDfTEE0/ogw8+UEZGhm6//XalpKTo6NGjysvLK9Nl2GE9Hx+fgk4oMjJSISEhmjlzpk6dOqW33nrL\n2+WhGKGhoWrcuLG+++47ffH6PatlAAAFLElEQVTFFxo9ejQd7TWASwdZ4PDhw/rqq6/0zTffKCAg\nQC+88IJuuukmb5cFF34OpXXr1ukvf/mLZsyYoeuvv97bZaEYR44cUdeuXXX99dfrzTff5PO6RhBI\nFjp37pycTqeqVq3q7VJQgry8PP3rX//Sddddp0aNGnm7HJQgPz9fM2fOVI8ePdSwYUNvlwM3IZCA\n/8NGhvIlNzeXafBrDIEEADACu+wAAEYgkAAARiCQAABGIJDgNSkpKWrevLliYmIUExOj6OhoDR8+\nXGfPnv3N77lo0SLFxsZKkl588UWlpqa6HLtt27YyXTk6NzdXN9988xWPT58+XVOnTi32tR07dtSh\nQ4dKfazY2FgtWrSo1OOBawGBBK8KCQlRfHy84uPjtXDhQtWqVUuzZs1yy3tPnTpVYWFhLp9fsmQJ\nX2UAGIQ9kzDKnXfeqU8//VTS5a4iKipKhw8f1rRp07RixQp9+OGHcjqdCgkJ0Wuvvabg4GB99NFH\n+uSTT1S7dm3VqlWr4L06duyo+fPnq379+nrttde0a9cuSdKTTz4pPz8/rVq1Sjt27NDLL7+shg0b\nasKECcrKylJmZqb+9Kc/qU2bNvrxxx81YsQIBQYG6q677iqx/o8//lhLly6Vv7+/KlSooKlTpxac\nh7Zo0SLt3LlT6enpGjNmjO666y4dPXq0yOMCdkQgwRh5eXn68ssvdfvttxc81qhRI40YMULHjh3T\n7NmzlZCQoICAAL3//vuaM2eOnn32WU2bNk2rVq1ScHCwBg8erGrVqhV638TERJ08eVKfffaZzp49\nq5deekmzZs1SkyZNNHjwYLVu3VoDBgzQU089pVatWiktLU19+/bVmjVrNHPmTPXs2VP9+vXTmjVr\nSvwdsrOzNXfuXAUFBWns2LFKTEzUY489JkmqXr263n//fSUnJysuLk5LlizR+PHjizwuYEcEErzq\n1KlTiomJkXT57Ps77rhDTzzxRMHzERERkqTt27crLS1N/fv3lyRdunRJ9erV06FDhxQeHq7g4GBJ\n0l133aW9e/cWOsaOHTsKupuqVavq73//+xV1bNq0SRcuXNDMmTMlSX5+fkpPT9e+ffs0YMAASVKr\nVq1K/H2qV6+uAQMGyMfHR0eOHFHNmjULnmvbtm3B77R///5ijwvYEYEEr/p5DckVf39/SVJAQIBa\ntGihOXPmFHp+586dha6ukJ+ff8V7OByOIh//pYCAAE2fPv2Kb451Op0FX2tQ0tedHz9+XHFxcVq+\nfLlCQ0MVFxd3RR2/fk9XxwXsiE0NKBduueUW7dixQ2lpaZKklStXau3atWrQoIFSUlJ09uxZOZ1O\nJScnX/HaiIgIrV+/XpJ0/vx59e7dW5cuXZLD4VBOTo4k6fbbb9fKlSslXe7aJk2aJEm64YYb9N13\n30lSke/9S+np6QoODlZoaKhOnz6tDRs26NKlSwXPb9y4UdLl3X033nhjsccF7IgOCeVCWFiYXnnl\nFQ0cOFCBgYGqWLGi4uLiVK1aNQ0aNEiPPvqowsPDFR4erosXLxZ6bVRUlLZt26bo6Gjl5eXpySef\nVEBAgNq2batx48Zp1KhReuWVVzR27FgtX75cly5d0uDBgyVJzz77rEaOHKlVq1YpIiKi2GunNWnS\nRA0bNlSvXr3UoEEDPf/88xo/frzatWsnSTp9+rQGDhyoo0ePaty4cZLk8riAHXEtOwCAEZiyAwAY\ngUACABiBQAIAGIFAAgAYgUACABiBQAIAGIFAAgAYgUACABjh/wFCz14YkACR4wAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f670d1f0240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FAyjh3bieLjn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Dropout"
      ]
    },
    {
      "metadata": {
        "id": "z6kLwcBveLy5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A great technique to overcome overfitting is to increase the size of your data but this isn't always an option. Fortuntely, there are methods like regularization and dropout that can help create a more robust model. We've already seen regularization and we can easily add it in our optimizer to use it in PyTorch. \n",
        "\n",
        "Dropout is a technique (used only during training) that allows us to zero the outputs of neurons. We do this for p% of the total neurons in each layer and it changes every batch. Dropout prevents units from co-adapting too much to the data and acts as a sampling strategy since we drop a different set of neurons each time.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/dropout.png\" width=400>"
      ]
    },
    {
      "metadata": {
        "id": "yGQq0MvcgBEG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Arguments\n",
        "args.dropout_p = 0.1 # 40% of the neurons are dropped each pass\n",
        "args.lambda_l2 = 1e-4 # L2 regularization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R6NvhBUyf27y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, dropout_p):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.dropout = nn.Dropout(dropout_p) # Defining the dropout\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def init_weights(self):\n",
        "        init.xavier_normal(self.fc1.weight, gain=nn.init.calculate_gain('relu'))\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        z = F.relu(self.fc1(x_in))\n",
        "        z = self.dropout(z) # dropping neurons\n",
        "        y_pred = self.fc2(z)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XQK9h7BNf26K",
        "colab_type": "code",
        "outputId": "3cc6e22b-2c63-4799-fac9-96673f4c7fbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=args.dimensions, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=args.num_classes, \n",
        "            dropout_p=args.dropout_p)\n",
        "print (model.named_modules)\n",
        "\n",
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate, \n",
        "                       weight_decay=args.lambda_l2) # Adding L2 regularization\n",
        "\n",
        "# Training\n",
        "pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method Module.named_modules of MLP(\n",
            "  (fc1): Linear(in_features=2, out_features=242, bias=True)\n",
            "  (dropout): Dropout(p=0.1)\n",
            "  (fc2): Linear(in_features=242, out_features=3, bias=True)\n",
            ")>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L0aQUomQoni1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Additional resources"
      ]
    },
    {
      "metadata": {
        "id": "bzYGBzEWV9a3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "- interpretability (easy w/ at with binary tasks)\n",
        "- dropconnect (but not really used)\n",
        "- PReLU activation function"
      ]
    }
  ]
}
